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Information Technology in Health Care – What the Future Holds

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Information Technology in Health Care – What the Future Holds

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Information Technology in Health Care – What the Future Holds on JSTOR

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AM Bulgiba, Information Technology in Health Care – What the Future Holds, Asia Pacific Journal of Public Health, Vol. 16, No. 1 (January 2004), pp. 64-71

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3. Artificial intelligence is changing the health sector from WHO Consultation Towards the Development of guidance on ethics and governance of artificial intelligence for health: Meeting report Geneva, Switzerland, 2–4 October 2019 on JSTOR

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JSTOR is a digital library of academic journals, books, and primary sources.

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PHARMACEUTICALS AND VACCINES from Biodata and biotechnology: Opportunity and challenges for Australia on JSTOR

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JSTOR is a digital library of academic journals, books, and primary sources.

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Chapter 4: Technology from Do No Harm 2.0 on JSTOR

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JSTOR is a digital library of academic journals, books, and primary sources.

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how AI will take jobs and jobs it will create and economic impact

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I am going to work off of Neil's discussion about what process AI have changed in primary care by discussing what process it may continue to change in the future. I will also discuss what future jobs it will create.

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I am going to work off of Neil's discussion about what process AI have changed in primary care by discussing what process it may continue to change in the future. I will also discuss what future jobs it will create.

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How does AI effect the consumer with pricing and easy of use.

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How does AI effect the consumer with pricing and easy of use.

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Data on how people would be affected and feel about A.I assisting them in different ways

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How AI changes the process in Primary Healthcare (Current)

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Artificial Intelligence, Humanistic Ethics

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Artificial Intelligence, Humanistic Ethics

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Artificial Intelligence, Humanistic Ethics on JSTOR

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John Tasioulas, Artificial Intelligence, Humanistic Ethics, Daedalus, Vol. 151, No. 2, AI & Society (Spring 2022), pp. 232-243

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Artificial intelligence is changing the health sector

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Artificial intelligence is changing the health sector

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3. Artificial intelligence is changing the health sector from WHO Consultation Towards the Development of guidance on ethics and governance of artificial intelligence for health: Meeting report Geneva, Switzerland, 2–4 October 2019 on JSTOR

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JSTOR is a digital library of academic journals, books, and primary sources.

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Artificial Intelligence for Cybersecurity: Technological and Ethical Implications

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Artificial Intelligence for Cybersecurity: Technological and Ethical Implications

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Artificial Intelligence for Cybersecurity: Technological and Ethical Implications Technological and Ethical Implications from DIGITAL THREATS SYMPOSIUM — FALL 2017 — COMPENDIUM OF PROCEEDINGS on JSTOR

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J-stor, why A.i is needed in healthcare and what problems it could assist in

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J-stor, why A.i is needed in healthcare and what problems it could assist in

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Chapter 5: Workforce from Do No Harm 2.0 on JSTOR

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JSTOR is a digital library of academic journals, books, and primary sources.

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Ethical standpoint (THE article to use for Ethics)

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Ethical standpoint (THE article to use for Ethics)

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60% of Americans Would Be Uncomfortable With Provider Relying on AI in Their Own Health Care on JSTOR

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JSTOR is a digital library of academic journals, books, and primary sources.

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Chapter 5: Workforce from Do No Harm 2.0 on JSTOR

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PHARMACEUTICALS AND VACCINES from Biodata and biotechnology: Opportunity and challenges for Australia on JSTOR

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Technological cybersecurity skills are in high demand for the maintenance of healthcare AI, meaning there will be many new job openings for cybersecurity in the primary care system. quote "The technical vulnerabilities in healthcare systems are compounded by another challenge: finding people with the necessary cybersecurity skills to protect those systems."

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Technological cybersecurity skills are in high demand for the maintenance of healthcare AI, meaning there will be many new job openings for cybersecurity in the primary care system. quote "The technical vulnerabilities in healthcare systems are compounded by another challenge: finding people with the necessary cybersecurity skills to protect those systems."

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Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

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Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the…

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"An AI system could identify an uncommon set of symptoms or bacterial strains much faster than a physician or laboratory. Which, for new diseases or outbreaks, could be the difference between a containable epidemic, or an uncontrolled pandemic. Evidence of such potential is shown in a study by the University of Oxford, in which AI identified viruses by analyzing their fluorescent"  labels."

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"An AI system could identify an uncommon set of symptoms or bacterial strains much faster than a physician or laboratory. Which, for new diseases or outbreaks, could be the difference between a containable epidemic, or an uncontrolled pandemic. Evidence of such potential is shown in a study by the University of Oxford, in which AI identified viruses by analyzing their fluorescent"  labels."

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10 Common Applications of Artificial Intelligence in Health Care

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10 Common Applications of Artificial Intelligence in Health Care

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10 Common Applications of Artificial Intelligence in Health Care - Novatio Solutions

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Many industries have been disrupted by the influx of new technologies in the Information Age. Healthcare is no different.Particularly in the case of automation, machine learning, and artificial intelligence (AI), doctors, hospitals, insurance companies, and industries with ties to healthcare have all been impacted – in many cases in more positive, substantial ways than other […]

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Professionals believe that in the future the best way to utilize Ai in health care would be to allow patients the choice of whether or not they would be willing to let AI have access to their information. Would be done to reduce the fallout of data leaks about privacy. Source "A proposed recommendation for resolving data accessibility for AI is to implement it only for participants willing to share their health information with ML systems which may maintain data  privacies."

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Professionals believe that in the future the best way to utilize Ai in health care would be to allow patients the choice of whether or not they would be willing to let AI have access to their information. Would be done to reduce the fallout of data leaks about privacy. Source "A proposed recommendation for resolving data accessibility for AI is to implement it only for participants willing to share their health information with ML systems which may maintain data  privacies."

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Another thing that will change is that with the inevitability of Ai's advancing information processing abilities they would begin to decrease the amount of mistakes made in comparison to humans.

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Another thing that will change is that with the inevitability of Ai's advancing information processing abilities they would begin to decrease the amount of mistakes made in comparison to humans.

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A 2018 World Economic Forum report projected AI would create a net sum of 58 million jobs by 2022. However, this same study finds 75 million jobs will be displaced or destroyed by AI by the same year. The major reason for this elimination of job opportunities is, as AI is more integrated across different sectors, roles that entail repetitive tasks will be redundant.

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No matter the industry, artificial intelligence (AI) has become commonplace. When it comes to medicine, AI helps health practitioners to streamline tasks, improve operational efficiencies and simplify complex procedures. Large tech companies are investing more funding into AI healthcare innovations. For instance, Microsoft announced a five-year $40 million program in 2020 to address healthcare challenges. Although AI is doubtlessly changing the healthcare industry, this technology is still relatively new. As AI adoption expands throughout the healthcare sector, questions about the advantages and limitations of this technology become ever more pertinent.

How Does AI Help Healthcare?

1. Provides Real-Time Data

A critical component of diagnosing and addressing medical issues is acquiring accurate information in a timely manner. With AI, doctors and other medical professionals can leverage immediate and precise data to expedite and optimize critical clinical decision-making. Generating more rapid and realistic results can lead to improved preventative steps, cost-savings and patient wait times.

Real-time analytics can help improve physician-patient relationships. Making vital patient data available through mobile devices can engage patients in their treatments. Mobile alerts can inform doctors and nurses of urgent changes in patient statuses and emergencies.

Drexel University Information Science Professor Christopher C. Yang, PhD, says, "As AI technology is becoming more advanced, more data can be collected than traditional medical institutions could ever possibly accumulate."

2. Streamlines Tasks

Artificial intelligence in medicine has already changed healthcare practices everywhere. Innovations include appointment-scheduling, translating clinical details and tracking patient histories. AI is enabling healthcare facilities to streamline more tedious and meticulous tasks. For example, intelligent radiology technology is able to identify significant visual markers, saving hours of intense analysis. Other automated systems exist to automate appointment scheduling, patient tracking and care recommendations.

One specific task that is streamlined with AI is reviewing insurance. AI is used to minimize costs resulting from insurance claim denials. With AI, health providers can identify and address mistaken claims before insurance companies deny payment for them. Not only does this streamline the claims process, AI saves hospital staff the time to work through the denial and resubmit the claim.

Enabling faster payments and greater claims accuracy, hospitals can be more confident about reimbursement time frames, making them more willing to accept a larger number of insurance plans. AI essentially allows hospitals to accept a wide array of plans, benefiting potential and existing patients.

3. Saves Time and Resources

As more vital processes are automated, medical professionals have more time to assess patients and diagnose illness and ailment. AI is accelerating operations to save medical establishments precious productivity hours. In any sector, time equals money, so AI has the potential to save hefty costs.

It's estimated around $200 billion is wasted in the healthcare industry annually. A good portion of these unnecessary costs are attributed to administrative strains, such as filing, reviewing and resolving accounts. Another area for improvement is in medical necessity determination. Hours of reviewing patient history and information are traditionally needed to properly assess medical necessity. New natural language processing (NLP) and deep learning (DL) algorithms can assist physicians in reviewing hospital cases and avoiding denials.

By freeing vital productivity hours and resources, medical professionals are allotted more time to assist and interface with patients.

4. Assists Research

AI enables researchers to amass large swaths of data from various sources. The ability to draw upon a rich and growing information body allows for more effective analysis of deadly diseases. Related to real-time data, research can benefit from the wide body of information available, as long as it's easily translated.

Medical research bodies like the Childhood Cancer Data Lab are developing useful software for medical practitioners to better navigate wide collections of data. AI has also been used to assess and detect symptoms earlier in an illness's progression. Telehealth solutions are being implemented to track patient progress, recover vital diagnosis data and contribute population information to shared networks.

5. May Reduce Physician Stress

Some latest research reports over half of primary physicians feel stressed from deadline pressures and other workplace conditions. AI helps streamline procedures, automate functions, instantly share data and organize operations, all of which help relieve medical professionals of juggling too many tasks.

Yang explains, "The most significant contributor to physician burn out is patient load and the nature of the profession. However, as AI can assist with more time-intensive operations, explaining diagnoses for example, medical professionals may experience some stress alleviation."

Limits of AI in Medicine

1. Needs Human Surveillance

Although AI has come a long way in the medical world, human surveillance is still essential. For example, surgery robots operate logically, as opposed to empathetically. Health practitioners may notice vital behavioral observations that can help diagnose or prevent medical complications.

"AI has been around for a few decades and continues to mature. As this area advances, there is more interaction between healthcare professionals and tech experts," Yang explains. AI requires human input and review to be leveraged effectively.

As AI develops, the tech and medical fields are increasingly communicating to improve the technology. Yang adds, "Years of education are required for medical professionals to operate in their fields. Essential information gathered from Subject Matter Experts (SMEs) enriches the data available and improves explainable AI (XAI) to provide healthcare workers with trusted and valuable insights."

2. May Overlook Social Variables

Patient needs often extend beyond immediate physical conditions. Social, economic and historical factors can play into appropriate recommendations for particular patients. For instance, an AI system may be able to allocate a patient to a particular care center based on a specific diagnosis. However, this system may not account for patient economic restrictions or other personalized preferences.

Privacy also becomes an issue when incorporating an AI system. Brands like Amazon have free reign when it comes to collecting and leveraging data. Hospitals, on the other hand, may face some set backs when attempting to channel data from Apple mobile devices, for instance. These regulatory and social restrictions may limit AI's ability to facilitate medical practices.

3. May Lead to Unemployment

Although AI may help cut costs and reduce clinician pressure, it may also render some jobs redundant. This variable may result in displaced professionals who invested time and money in healthcare education, presenting equity challenges.

A 2018 World Economic Forum report projected AI would create a net sum of 58 million jobs by 2022. However, this same study finds 75 million jobs will be displaced or destroyed by AI by the same year. The major reason for this elimination of job opportunities is, as AI is more integrated across different sectors, roles that entail repetitive tasks will be redundant.

Though AI promises to improve several aspects of healthcare and medicine, it's vital to consider the social ramifications of integrating this technology.

4. Inaccuracies Are Still Possible

Medical AI depends heavily on diagnosis data available from millions of catalogued cases. In cases where little data exists on particular illnesses, demographics, or environmental factors, a misdiagnosis is entirely possible. This factor becomes especially important when prescribing particular medicine.

Remarking on this data gap, Yang says, "No matter the system, there is always some portion of missing data. In the case with prescriptions, some information regarding certain populations and reactions to treatments may be absent. This occurrence can lead to issues with diagnosing and treating patients belonging to certain demographics."

AI is constantly evolving and improving to account for data gaps. However, it's important to note that specific populations may still be excluded from existing domain knowledge.

5. Susceptible to Security Risks

As AI is generally dependent on data networks, AI systems are susceptible to security risks. The onset of Offensive AI, improved cyber security will be required to ensure the technology is sustainable. According to Forrester Consulting, 88% of decision-makers in the security industry are convinced offensive AI is an emerging threat.

As AI uses data to make systems smarter and more accurate, cyberattacks will incorporate AI to become smarter with each success and failure, making them more difficult to predict and prevent. Once damaging threats out-maneuver security defenses, the attacks will be much more challenging to address.

Should Artificial Intelligence be Used in Healthcare?

AI has doubtless potential to improve healthcare systems. Automating tedious tasks can free up clinician schedules to allow for more patient interfacing. Improving data accessibility assists healthcare professionals in taking the right steps to prevent illness. Real-time data can better and more rapidly inform diagnoses. AI is being implemented to reduce administrative errors and save vital resources. SMEs are increasingly involved in AI development, making the technology more applicable and better-informed. AI is increasingly applied to healthcare, and limits and challenges continue to be confronted and overcome. AI still requires some human surveillance, may exclude social variables, experiences gaps in population information and is susceptible to increasingly-calculated cyberattacks. Despite some of the challenges and limits AI faces, this innovative technology promises extraordinary benefits to the medical sector. Whether a patient or physician, lives everywhere are improving thanks to AI.

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Pros & Cons of Artificial Intelligence in Medicine - College of Computing & Informatics

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AI in healthcare is no longer something out of a Sci-Fi novel, and it's in use today. Find out its applications, benefits, and risks from Professor Christopher Yang, PhD.

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3. Artificial intelligence is changing the health sector from WHO Consultation Towards the Development of guidance on ethics and governance of artificial intelligence for health: Meeting report Geneva, Switzerland, 2–4 October 2019 on JSTOR

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JSTOR is a digital library of academic journals, books, and primary sources.

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4 Digitalisation and the jobs of the future from The Realities and Futures of Work on JSTOR

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Digitalisation and the jobs of the future, The Realities and Futures of Work, pp. 83-112

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An economic perspective on Artificial Intelligence

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An economic perspective on Artificial Intelligence

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1 An economic perspective on Artificial Intelligence from The Brain and the Processor:: Unpacking the Challenges of Human-Machine Interaction on JSTOR

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JSTOR is a digital library of academic journals, books, and primary sources.

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10. Market Incentives from Advancing Accountability in Cyberspace:: Models, Mechanisms, and Multistakeholder Approaches on JSTOR

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JSTOR is a digital library of academic journals, books, and primary sources.

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3. Artificial intelligence is changing the health sector from WHO Consultation Towards the Development of guidance on ethics and governance of artificial intelligence for health: Meeting report Geneva, Switzerland, 2–4 October 2019 on JSTOR

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JSTOR is a digital library of academic journals, books, and primary sources.

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Overview of artificial intelligence in medicine : Journal of Family Medicine and Primary Care

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achines. Objective: This descriptive article gives a broad overview of AI in medicine, dealing with the terms and concepts as well as the current and future applications of AI. It aims to develop knowledge and familiarity of AI among primary care physicians. Materials and Methods: PubMed and Google searches were performed using the key words ‘artificial intelligence’. Further references were obtained by cross-referencing the key articles. Results: Recent advances in AI technology and its current applications in the field of medicine have been discussed in detail. Conclusions: AI promises to change the practice of medicine in hitherto unknown ways, but many of its practical applications are still in their infancy and need to be explored and developed better. Medical professionals also need to understand and acclimatize themselves with these advances for better healthcare delivery to the masses.…

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Artificial intelligence simplifies the lives of patients, doctors and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost.

AI in healthcare shows up in a number of ways, such as finding new links between genetic codes, powering surgery-assisting robots, automating administrative tasks, personalizing treatment options and much more.

Put simply, AI is reinventing - and reinvigorating - modern healthcare through machines that can predict, comprehend, learn and act.

To give you a better understanding of the rapidly evolving field, we rounded up some of the top examples of AI in healthcare - as well as the companies that are leading the way.

Image: Shutterstock

Examples of AI in Medical Diagnosis

Every year, roughly 400,000 hospitalized patients suffer preventable harm, with 100,000 deaths. In light of that, the promise of improving the diagnostic process is one of AI's most exciting healthcare applications.

Incomplete medical histories and large caseloads can lead to deadly human errors. Immune to those variables, AI can predict and diagnose disease at a faster rate than most medical professionals.

Here are 13 examples of companies showing AI's ability to reduce errors and save lives.

Location: Boston, Massachusetts

With its early-detection platform for cognitive assessments, Linus Health is on a mission to modernize brain health. Its proprietary assessment technology DCTclock takes the gold standard pen-and paper clock drawing test for early signs of cognitive impairment and digitizes it, bringing together the most recent advances in neuroscience and AI to analyze over 50 metrics that reflect the patient's cognitive function.

Location: San Francisco, California

In healthcare, delays can mean the difference between life and death, so Viz.ai helps care teams react faster with AI-powered healthcare solutions. The company's AI products can detect issues and notify care teams quickly, enabling providers to discuss options, provide faster treatment decisions, thus saving lives.

Location: Boston, Massachusetts

PathAI develops machine learning technology to assist pathologists in making more accurate diagnoses. The company's current goals include reducing error in cancer diagnosis and developing methods for individualized medical treatment. PathAI worked with drug developers like Bristol-Myers Squibb and organizations like the Bill & Melinda Gates Foundation to expand its AI technology into other healthcare industries.

Location: Los Angeles, California

The healthtech startup Regard uses AI technology to diagnose patients. The company describes its automated system to be the "clinical co-pilot" to electronic medical records (EMRs). The data from EMRs is synthesized to discover a diagnosis. Additionally, healthcare providers receive specific recommendations about patient care. The system also updates patient documents automatically to reduce burnout among healthcare workers.

Read More 94 Top Healthcare Startups and Healthtech Companies

Location: Boston, Massachusetts

Developed by a team out of Harvard Medical School, Buoy Health is an AI-based symptom and cure checker that uses algorithms to diagnose and treat illness. Here's how it works: a chatbot listens to a patient's symptoms and health concerns, then guides that patient to the correct care based on its diagnosis.

Location: Fort Collins, Colorado

Enlitic develops deep learning medical tools to streamline radiology diagnoses. The company's deep learning platform analyzes unstructured medical data - radiology images, blood tests, EKGs, genomics, patient medical history - to give doctors better insight into a patient's real-time needs.

Location: San Francisco, California

Freenome uses AI in screenings, diagnostic tests and blood work to test for cancer. By deploying AI at general screenings, Freenome aims to detect cancer in its earliest stages and subsequently develop new treatments.

Location: Boston, Massachusetts

Harvard University's teaching hospital, Beth Israel Deaconess Medical Center, used AI for diagnosing potentially deadly blood diseases at an early stage. Doctors developed AI-enhanced microscopes to scan for harmful bacterias like E. coli and staphylococcus in blood samples at a faster rate than is possible using manual scanning. The scientists used 25,000 images of blood samples to teach the machines how to search for bacteria. The machines then learned how to identify and predict harmful bacteria in blood with 95 percent accuracy.

Location: Cambridge, Massachusetts

Iterative Health applies AI to gastroenterology to improve disease diagnosis and treatment. The company's AI Recruitment service uses computational algorithms to automate the process of identifying patients who are eligible to be potential candidates for inflammatory bowel disease clinical trials. Iterative Health also produces SKOUT, a tool that uses AI to help doctors identify potentially cancerous polyps.

Location: Peoria, Illinois

VirtuSense uses AI sensors to track a patient's movements so that providers and caregivers can be notified of potential falls. The company's products include VSTAlert, which can predict when a patient intends to stand up and notify appropriate medical staff, and VST Balance, which employs AI and machine vision to analyze a person's risk of falling within the next year.

Location: San Mateo, California

Caption Health combines AI and ultrasound technology for early disease identification. AI guides providers through the ultrasound process in real time to produce diagnostic-quality images that the software then helps to interpret and assess.

Location: San Francisco, California

Arterys creates products for precision medicine. Its medical imaging AI platform can be used for detecting breast cancer, analyzing cardiac MRI images, reading emergency department X-rays, tracking lung nodules, diagnosing brain tumors and detecting strokes.

Location: Fully Remote

Cleerly makes AI technology to improve cardiovascular care. The company's AI-enabled digital care platform measures and analyzes atherosclerosis, which is a buildup of plaque in the heart's arteries. The technology is able to determine an individual's risk of having a heart attack and recommend a personalized treatment plan.

Image: Shutterstock

Examples of AI in Drug Discovery

The drug development industry is bogged down by skyrocketing development costs and research that takes thousands of human hours. Putting each drug through clinical trials costs an estimated average of $1.3 billion, and only 10 percent of those drugs are successfully brought to market.

Due to breakthroughs in technology, biopharmaceutical companies are quickly taking notice of the efficiency, accuracy and knowledge AI can provide.

Here are some companies that rely on AI to develop the next wave of medicines.

Location: New Haven, Connecticut

BioXcel Therapeutics uses AI to identify and develop new medicines in the fields of immuno-oncology and neuroscience. Additionally, the company's drug re-innovation program employs AI to find new applications for existing drugs or to identify new patients.

Location: Cambridge, Massachusetts

Reverie Labs is a pharmaceutical company harnessing computational chemistry and machine learning tools for drug discovery and design. It uses predictive analytic s tools and expansive databases, with the ultimate goal of learning more about cancer and developing effective cancer treatments.

Read Next 10 Examples of Wearable Technology in Healthcare and Wearable Medical Devices

Location: Boston, Massachusetts

Valo uses artificial intelligence to achieve its mission of transforming the drug discovery and development process. With its Opal Computational Platform, Valo collects human-centric data to identify common diseases among a specific phenotype, genotype and other links, which eliminates the need for animal testing. The company then establishes the molecule design and clinical development.

Location: Cambridge, Massachusetts

Combining AI, the cloud and quantum physics, XtalPi 's ID4 platform predicts the chemical and pharmaceutical properties of small-molecule candidates for drug design and development. The company's investors have included Google, Tencent and Sequoia Capital.

Location: San Francisco, California

Atomwise uses AI to tackle serious diseases, including Ebola and multiple sclerosis. The company's neural network, AtomNet, helps predict bioactivity and identify patient characteristics for clinical trials. Atomwise's AI technology screens between 10 and 20 million genetic compounds each day and can reportedly deliver results 100 times faster than traditional pharmaceutical companies.

Location: Fully Remote

Deep Genomics ' AI platform helps researchers find candidates for developmental drugs related to neuromuscular and neurodegenerative disorders. Finding the right candidates during a drug's development statistically raises the chances of successfully passing clinical trials while also decreasing time and cost to market.

Location: London, England

The primary goal of BenevolentAI is to get the right treatment to the right patients at the right time by using AI to produce a better target selection and provide previously undiscovered insights through deep learning. BenevolentAI works with major pharmaceutical groups to license drugs, while also partnering with charities to develop easily transportable medicines for rare diseases.

Location: Menlo Park, California

Deepcell uses artificial intelligence and microfluidics to develop technology for single cell morphology. The company's platform has a variety of applications, including cancer research, cell therapy and developmental biology.

Location: New York, New York

Owkin leverages AI technology for drug discovery and diagnostics with the goal of enhancing cancer treatment. The company's AI tools help identify new drug targets, recommend possible drug combinations and suggest additional diseases that a drug can be repurposed to treat. Owkin also produces RlapsRisk, a diagnostic tool for assessing a breast cancer patient's risk of relapse, and MSIntuit, a tool that assists with screening for colorectal cancer.

Image: Shutterstock

Examples of AI Transforming Patient Experience

In the healthcare industry, time is money. Efficiently providing a seamless patient experience allows hospitals, clinics and physicians to treat more patients on a daily basis. Studies show hospitals whose patients have positive experiences have higher profits, while negative reviews can lead to financial losses.

We've rounded up some examples of how AI is helping healthcare facilities better manage patient flow.

Location: Austin, Texas

With the goal of improving patient care, Iodine Software is creating AI-powered and machine-learning solutions for mid-revenue cycle leakages, like resource optimization and increased response rates. The company's CognitiveML product discovers client insights, ensures documentation accuracy and highlights missing information.

Location: New York, New York

Kaia Health operates a digital therapeutics platform that features live physical therapists to provide people care within the boundaries of their schedules. The platform includes personalized programs with case reviews, exercise routines, relaxation activities and learning resources for treating chronic back pain and COPD. Kaia Health also features a PT-grade automated feedback coach that uses AI technology.

Location: New York, New York

Spring Health offers a mental health benefit solution employers can adapt to provide their employees with the resources to keep their mental health in check. The technology works by collecting a comprehensive dataset from each individual and comparing that against hundreds of thousands of other data points. The platform then uses a machine learning model to match people with the right specialist for either in-person care or telehealth appointments.

Location: Mountain View, California

Twin Health's holistic method seeks to address and potentially reverse chronic conditions like Type 2 Diabetes through a mixture of IoT tech, AI, data science, medical science and healthcare. The company created the Whole Body Digital Twin - a digital representation of human metabolic function built around thousands of health data points, daily activities and personal preference.

Location: Columbus, Ohio

Olive 's AI platform automates repetitive healthcare tasks, freeing up administrators to work on higher-level ones. The platform automates everything from eligibility checks to un-adjudicated claims and data migrations so staffers can focus on patient service.

Location: Mountain View, California

Qventus is an AI-based software platform that solves operational challenges, including those related to emergency rooms and patient safety. The company's automated platform can prioritize patient illness and injury and tracks hospital waiting times to help hospitals and health systems optimize care delivery.

Location: Cleveland, Ohio

The Cleveland Clinic teamed up with IBM on the Discovery Accelerator, an AI-infused initiative focused on faster healthcare breakthroughs. The joint center is building an infrastructure that supports research in areas such as genomics, chemical and drug discovery and population health. The collaboration employs big data medical research for the purpose of innovating patient care and approaches to public health threats.

Location: Baltimore, Maryland

Johns Hopkins Hospital partnered with GE Healthcare to use predictive AI techniques to improve the efficiency of patient operational flow. A task force, augmented with AI, quickly prioritized hospital activity to benefit patients. Since implementing the program, the facility has assigned patients admitted to the emergency department to beds 38 percent faster.

Location: Austin, Texas

Babylon is on a mission to reengineer healthcare by shifting the focus away from caring for the sick to helping prevent sickness, leading to better health and less health-related expenses. The platform features an AI engine created by doctors and deep learning scientists that operates an interactive symptom checker, using known symptoms and risk factors to provide the most informed and up-to-date medical information possible.

Location: New York, New York

One Drop provides a discreet solution for managing chronic conditions like diabetes and high blood pressure, as well as weight management. The One Drop Premium app allows people to manage their conditions head first, offering interactive coaching from real-world professionals, predictive glucose readings powered by AI and data science, learning resources and daily tracking of readings taken from One Drop's Bluetooth-enabled glucose reader and other devices.

Location: Palo Alto, California

CloudMedX uses machine learning to generate insights for improving patient journeys throughout the healthcare system. The company's technology helps hospitals and clinics manage patient data, clinical history and payment information by using predictive analytics to intervene at critical junctures in the patient care experience. Healthcare providers can use these insights to efficiently move patients through the system.

Location: Menlo Park, California

Subtle Medical uses AI to enhance images for radiology departments. The SubtlePET and SubtleMR products work with the machines a facility already uses to speed up MRI and PET scans while reducing image noise. The software has the potential to shrink wait times by scanning more patients each day.

Location: New York, New York

Twill describes itself as "The Intelligent Healing Company," delivering digital healthcare products and partnering with enterprises, pharma companies and health plans to develop products using its Intelligent Healing Platform. The company uses AI to tailor personalized care tracks for managing medical conditions like multiple sclerosis and psoriasis. These individualized programs can include digital therapeutics, care communities and coaching options.

Location: San Francisco, California

Augmedix offers a suite of AI-enabled medical documentation tools for hospitals, health systems, individual physicians and group practices. The company says its products use natural language processing and automated speech recognition to save users time, increase productivity and improve patient satisfaction.

Further Reading Cloud Computing in Healthcare: How It's Used and 10 Examples

Image: Shutterstock

Examples of AI for Healthcare Data Management

Healthcare is widely considered one of the next big data frontiers to tame. Highly valuable information can sometimes get lost among the forest of trillions of data points. Additionally, the inability to connect important data points slows the development of new drugs, preventative medicine and proper diagnosis.

Many in healthcare are turning to AI as a way to stop the data hemorrhaging. The technology breaks down data silos and connects in minutes information that used to take years to process.

Here are eight examples of AI companies helping the healthcare industry stay afloat in an ocean of data.

Location: Chicago, Illinois

Tempus uses AI to sift through the world's largest collection of clinical and molecular data to personalize healthcare treatments. The company develops AI tools that give physicians insights into treatments and cures.

Location: Armonk, New York

Once known as a Jeopardy-winning supercomputer, IBM's Watson now helps healthcare professionals harness their data to optimize hospital efficiency, better engage with patients and improve treatment. Watson applies its skills to everything from developing personalized health plans to interpreting genetic testing results and catching early signs of disease.

Location: Austin, Texas

ClosedLoop.ai is an end-to-end platform that uses AI to discover at-risk patients and recommend treatment options. Through the platform, healthcare organizations can receive personalized data about patients' needs while collecting looped feedback, outreach and engagement strategies and digital therapeutics. The platform can be used by healthcare providers, payers, pharma and life science companies.

Location: Fully Remote

Beacon Biosignals aims to treat neurological and psychiatric diseases through its EEG analytics platform, which utilizes portal reporting, standardized neurobiomarkers and machine learning algorithms that "increase the probability of success at each stage of drug development." Additionally, population stratification is used to identify various patient populations.

Location: Philadelphia, Pennsylvania

Proscia is a digital pathology platform that uses AI to detect patterns in cancer cells. The company's software helps pathology labs eliminate bottlenecks in data management and uses AI-powered image analysis to connect data points that support cancer discovery and treatment.

Location: Mountain View, California

H2O.ai 's AI analyzes data throughout a healthcare system to mine, automate and predict processes. It has been used to predict ICU transfers, improve clinical workflows and pinpoint a patient's risk of hospital-acquired infections. Using the company's AI to mine health data, hospitals can predict and detect sepsis, which ultimately reduces death rates.

Location: Shenzhen, China

ICarbonX uses AI and big data to look more closely at human life characteristics with its Digital Life Platform. By analyzing the health and actions of human beings in a "carbon cloud," the company hopes its big data will help manage all aspects of health. ICarbonX believes its technology can gather enough data to better classify symptoms, develop treatment options and get people healthier.

Location: San Francisco, California

AKASA 's AI platform helps healthcare providers streamline workflows by automating administrative tasks to allow staff to focus where they're needed. The automation can be customized to meet a facility's particular needs and priorities, while maintaining accuracy for managing claims, payments and other elements of the revenue cycle.

Image: Shutterstock

Examples of AI in Robotic Surgery

Popularity in robot-assisted surgery is skyrocketing. Hospitals are using robots to help with everything from minimally invasive procedures to open heart surgery. According to the Mayo Clinic, robots help doctors perform complex procedures with a precision, flexibility and control that goes beyond human capabilities.

Surgeons can control a robot's mechanical arms while seated at a computer console as the robot gives the doctor a three dimensional, magnified view of the surgical site that surgeons could not get from relying on their eyes alone. The surgeon then leads other team members who work closely with the robot through the entire operation.

Robot-assisted surgeries have led to fewer surgery-related complications, less pain and a quicker recovery time. Take a look at five examples of how robots are shaping the future of surgery.

Location: Waltham, Massachusetts

Vicarious Surgical combines virtual reality with AI-enabled robots so surgeons can perform minimally invasive operations. Using the company's technology, surgeons can virtually shrink and explore the inside of a patient's body in detail. Vicarious Surgical's technology concept prompted former Microsoft chief Bill Gates to invest in the company.

Read More Blockchain in Healthcare: 17 Examples to Know

Location: Sunnyvale, California

The Accuray CyberKnife system uses AI and robotics to precisely treat cancerous tumors. The technology lets providers personalize stereotactic radiosurgery and stereotactic body radiation therapy for each patient. Using the robot's real-time tumor tracking capabilities, doctors and surgeons can treat affected areas rather than the whole body.

Location: Sunnyvale, California

The first robotic surgery assistant approved by the FDA, Intuitive 's da Vinci platforms feature cameras, robotic arms and surgical tools to aid in minimally invasive procedures. Da Vinci platforms constantly take in information and provide analytics to surgeons to improve future procedures. Da Vinci has assisted in over 10 million operations.

Location: Pittsburgh, Pennsylvania

The Robotics Institute at Carnegie Mellon University developed HeartLander, a miniature mobile robot designed to facilitate therapy on the heart. Under a physician's control, the tiny robot enters the chest through a small incision, navigates to certain locations of the heart by itself, adheres to the surface of the heart and administers therapy.

Location: Eindhoven, Netherlands

Microsure 's robots help surgeons overcome their human physical limitations. The company's motion stabilizer system is intended to improve performance and precision during surgical procedures. Its MUSA surgical robot, developed by engineers and surgeons, can be controlled via joysticks for performing microsurgery.

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Artificial Intelligence in Healthcare: 39 Examples Improving the Future of Medicine

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Artificial intelligence simplifies the lives of patients, doctors and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost. AI in healthcare shows up in a number of ways, such as finding new links between genetic codes, powering surgery-assisting robots, automating administrative tasks, personalizing treatment options and much more.

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Applications of Artificial Intelligence (AI) in healthcare: A review

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Artificial intelligence is revolutionizing — and strengthening — modern healthcare through technologies that can predict, grasp, learn, and act, whether it’s employed to identify new relationships between genetic codes or to control surgery-assisting robots. It can detect minor patterns that humans would completely overlook. This study explores and discusses the various modern applications of AI in the health sector. Particularly, the study focuses on three most emerging areas of AI-powered healthcare: AI-led drug discovery, clinical trials, and patient care. The findings suggest that pharmaceutical firms have benefited from AI in healthcare by speeding up their drug discovery process and automating target identification. Artificial Intelligence (AI) can help also to eliminate time-consuming data monitoring methods. The findings also indicate that AI-assisted clinical trials are capable of handling massive volumes of data and producing highly accurate results. Medical AI companies develop systems that assist patients at every level. Patients’ medical data is also analyzed by clinical intelligence, which provides insights to assist them improve their quality of life.

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Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA

School of Chemistry, Chemical Engineering, and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore

Author to whom correspondence should be addressed.

These authors contributed equally to this work.

1. Introduction

The origins of AI can be traced back to more than 70 years ago, specifically during World War II. It was during this time that mathematician Alan Turing developed The Bombe, an electro-mechanical computer spanning almost 50 square feet in size. A remarkable feat at the time, The Bombe successfully broke the Enigma code, a task previously believed to be impossible even for the most brilliant human mathematicians [ 1]. This pivotal moment in history ignited further inquiry into the potential of creating machines capable of incorporating external data and processing it using algorithms to produce useful and efficient outcomes for various tasks. In 1950, Alan Turing examined the possibility of creating intelligent computational machines that could imitate human thought processes. He developed a method known as the Turing test to assess whether a machine's responses can be distinguished from those of a human [ 2]. If the machine's answers are indistinguishable from a human's, it is considered intelligent. Several years later in 1956, John McCarthy coined the term "Artificial Intelligence", describing it as the computational aspect of achieving goals and capable of being precisely defined for simulation by machines [ 3].

Since the inception of AI, this field has experienced rapid advancement. Following its successful demonstration in passing the Turing test, AI began to make significant contributions across various industries. One early adopter was the automotive industry, which witnessed an infusion of AI technology starting in the early 1960s. In particular, John Devol and Joseph Engelberger pioneered the development of Unimate, a groundbreaking industrial robot introduced in 1961. Originally designed for die-casting tasks, it later found application in workpiece handling and spot-welding processes involved in car body manufacturing [ 4]. Subsequently, advancements were made towards creating conversational systems using chatbots powered by AI algorithms. In 1966, Joseph Weizenbaum introduced Eliza, the first chatbot. Eliza could engage in conversations with humans by utilizing pattern matching to generate appropriate responses based on textual inputs [ 5]. During the same period, Charles A. Rosen created Shakey, a pioneering achievement that enabled an electronic entity capable of executing complex tasks and identifying errors for correction [ 6]. These significant milestones played a crucial role in shaping AI's impact across industries and its subsequent expansion into healthcare applications.

During the period from the 1970s to the 2000s, there was a decline in significant advancements in AI known as the "AI winter". However, this downturn resulted in increased utilization of AI in the medical field [ 7]. Ophthalmology emerged as one of the early adopters of incorporating AI technology. A notable example was the development of CASNET/glaucoma model in 1976, which utilized stored patient data to make informed decisions based on physiological parameter changes, clinical manifestations, and treatment outcomes [ 8]. This model showcased the potential of AI in ophthalmology and paved the way for further advancements in the field. A similar system known as the INTERNIST-1 was developed to assist physicians in accurately diagnosing complex internal medicine diseases [ 9]. In 1996, the ZEUS robotic surgery system was introduced as the first complete remote surgery system, designed to enhance laparoscopic procedures by eliminating motion scaling and tremors [ 10]. These early advancements in AI within the medical field have sparked renewed interest in AI applications throughout medicine in the 21st century. Figure 1 illustrates the significant surge in AI research over the past decade.

In recent years, there has been a significant surge of interest in integrating AI into the medical domain. Continuous advancements and enhancements of AI have paved the way for its application in complex medical cases. For instance, in 2017 AI technology was introduced to cardiology through CardioAI, an innovative clinical design that utilizes deep learning algorithms to analyze cardiac magnetic resonance images rapidly. This groundbreaking development enables the swift determination of essential information such as cardiac ejection fraction within seconds [ 11]. Subsequently, similar techniques were extended to lung imaging analysis and interpretation of musculoskeletal system X-ray images as well as head CT scans. A noteworthy step forward occurred in 2019, when AI was utilized collaboratively with endoscopies, demonstrating promising potential for future growth while remaining subject to ongoing refinement and improvement. The use of computer-aided diagnosis (CAD) has enabled accurate detection, differentiation, and characterization of both neoplastic and non-neoplastic colon polyps, with an average accuracy rate of 91.5% [ 11]. Moreover, the field of AI in healthcare has witnessed the emergence of several companies that contribute to various aspects of medicine; for examples, see Table 1. Such companies support robotic surgeries, data collection from patients, and prompt diagnosis and treatment through blood tests for early-stage cancers. This highlights how AI has evolved into an effective tool from its inception in the 1950s and continues to hold potential for further advancements in addressing challenging tasks encountered within the medical domain.

This review contributes to the field by: (a) providing readers with a clear and concise understanding of the topic through a detailed overview of AI, including its origins, evolution, and current state, with a particular focus on its uses within healthcare; (b) reviewing and analyzing varied applications of AI in healthcare, ranging from diagnostics and medical imaging to surgical assistance, patient prognosis, and treatment decision-making; (c) discussing AI's role and impact during pandemic scenarios, demonstrating its potential in disease identification, monitoring patients, determining mortality risk, managing data, and facilitating rapid response, accurate diagnosis, and efficient treatment; (d) highlighting the potential advantages and challenges associated with the integration of AI in healthcare, including ethical, legal, and social considerations, while raising awareness about the need for regulated and ethically guided use of AI in medicine; (e) detailing the role of AI in contemporary innovations such as remote monitoring, telehealth, drug discovery, vaccine development, and natural language processing; and (f) providing a thorough view of AI's transformative potential in various medical fields, such as rehabilitation, gastroenterology, nutritional assessment, and surgery.

This paper is organized into several cohesive sections, each serving a specific purpose. (1) Introduction-introduces the topic at hand, establishes its relevance, and presents an overview of the current state of knowledge in the field. It also outlines the objectives of the paper. (2) Key principles of AI-describes the structured methodologies and advanced techniques used in AI, such as artificial neural networks and deep learning, and their successful applications in predictive modeling within the medical domain. (3) Key Applications of AI in Healthcare-explores the diverse applications of AI in health care, ranging from diagnostics and medical imaging to surgical assistance, patient prognosis, and treatment decision-making. (4) The Role of AI in Medicine-delves into the transformative potential of AI in various medical fields, touching upon areas such as rehabilitation, gastroenterology, nutritional assessment, and surgery, among others. (5) Considerations of AI Applications-tackles the crucial ethical, legal, and social considerations that arise from the integration of AI in healthcare, addressing issues related to accountability, liability, privacy protection, bias and discrimination, transparency, consent, and the balance between innovation and regulatory oversight. (6) Discussion-synthesizes the contents of the previous sections, contemplating the implications of the findings, examining potential challenges, and offering thoughts on future directions in the field of AI in healthcare. This structure ensures comprehensive coverage of the topic and aids readers in understanding the various aspects of the AI in healthcare.

2. Key Principles of AI

The field of AI utilizes a structured method to integrate external data, employing intricate algorithms that ultimately yield accurate predictions for specific scenarios [ 12]. This systematic process involves advanced techniques such as artificial neural networks and deep learning. By appropriately selecting and implementing the algorithm tailored to the technique being employed [ 13], AI has successfully found its applications in predictive modeling within the medical domain. Figure 2 depicts the use of neural networks for medical decision support. This process might encompass various steps such as collecting and preprocessing data, training the model, revising the model through cross-validation, choosing the best model, and integrating it with systems such as desktop applications or embedded devices and hardware.

2.1. Artificial Neural Networks

Artificial Neural Networks (ANNs) have gained popularity in the medical domain due to their effectiveness in diagnostics and imaging detection. They are commonly used for clinical diagnosis, including histopathology evaluations and electrocardiograms (ECGs). In addition, ANNs have been integrated into surgical diagnostic procedures covering aspects such as abdominal pain assessment, appendicitis identification, detection of retained common bile duct stones, and evaluation of glaucomas [ 14]. These networks draw inspiration from the nervous system and employ a sophisticated methodology for gathering input data that ensures accurate results are obtained. In the medical field, accurate diagnosis is crucial for effective treatment and patient care. As depicted in Figure 3, artificial neural networks comprise interconnected layers that guide input data towards the intended path in order to produce a reliable output. The input data are initially processed and gathered in the input layer, which captures general information. The data then traverse multiple subsequent layers (layer 1, layer 2, etc.), ensuring greater accuracy of the resulting output. Upon traversing these deeper layers, the data reach the final output layer, where an appropriate solution or answer is generated based on the provided input. ANNs effectively select optimal paths for data while minimizing inaccuracies by eliminating alternative pathways during processing. The application of ANNs in medical diagnosis is becoming increasingly prevalent, as they offer a promising solution for accurate and efficient diagnostics.

In the context of ANNs, "choosing the best path for the data" refers to the network's ability to evaluate input data and make informed decisions regarding which connections and pathways within the network should be activated. This process involves analyzing patterns and features in order to adjust connection strengths between neurons while giving priority to relevant information. By selecting an appropriate pathway through the network, the probability of producing accurate outputs is increased. To determine this optimal path, various factors are considered by the ANN, including the weights assigned to connections and the activation functions applied to the neurons. These considerations enable informed decision-making with the goal of minimizing errors or inaccuracies in the findings [ 15].

The "feed forward" model described here relies on a substantial amount of data to effectively determine the optimal pathway. Furthermore, this network commonly utilizes a backpropagation algorithm, in which information flows backwards from the deepest layer to match inputs and outputs by minimizing discrepancies between predicted values and known correct outputs [ 16]. In essence, the available data are meticulously analyzed to provide an accurate solution.

2.2. Deep Learning Networks

2.3. Machine Learning

2.4. Key Principles of AI within the Context of Healthcare

To reiterate the above within the context of healthcare, the relationships between ANNs, DLNs, and ML are described below to provide a more comprehensive understanding of their interplay within the field of AI. ANNs play a fundamental role in AI for healthcare applications. ANNs are computational models inspired by the structure and functioning of biological neural networks. They have been extensively utilized in various healthcare applications, including disease diagnosis, prognosis, and treatment prediction. They consist of interconnected artificial neurons that process and transmit information in a manner similar to the human brain. ANNs have demonstrated remarkable capabilities in pattern recognition, feature extraction, and nonlinear mapping, making them highly effective in analyzing complex medical data. DLNs build upon the foundations of ANNs, and have revolutionized the field of AI in healthcare. DLNs are composed of multiple layers of artificial neurons, enabling the creation of deep neural networks capable of learning hierarchical representations of data. This depth allows DLNs to automatically extract complex and abstract features from medical images, signals, and textual data. Their ability to learn from large-scale datasets has facilitated significant advancements in medical imaging analysis, natural language processing, and clinical decision support systems.

Additionally, different ML algorithms are particularly suited to different applications. For example, certain models might perform better in image analysis for diagnosis, while others might be more effective in forecasting disease spread. Comparing the efficacy of different ML models using the same datasets is of paramount importance in the field of AI. Such comparisons allow researchers and practitioners to gain insights into the strengths and weaknesses of various models, enabling them to make informed decisions about which approach to adopt for a given task. By applying multiple models to the same dataset, it becomes possible to assess their performance, accuracy, and generalization capabilities, e.g., [ 25]. This process promotes a better understanding of the underlying algorithms and their suitability for specific problem domains, and can aid in identifying the most effective and efficient solution. Additionally, comparing different models on the same datasets fosters fair and unbiased evaluations, contributing to the advancement of machine learning techniques and driving innovation in the field.

3. Key Applications of AI in Healthcare

The various subfields and modalities of AI discussed earlier have the potential to greatly impact different industries, leading to overall improvements and growth. The recent COVID-19 pandemic has created a pressing need for digital solutions, particularly in the medical field. Machine learning has found applications in tasks such as medical imaging, disease diagnosis, patient prognosis, and treatment decision-making [ 26, 27, 28]. Moreover, AI has been successfully integrated into other important aspects of healthcare such as virtual nursing assistants and reducing medication errors [ 29]. As shown in Table 2, the majority of AI applications are within the medical field due to several key factors that must be addressed for optimal patient care outcomes. Notably, these advancements hold significant financial value, with an estimated combined annual worth exceeding USD 100 billion over the next three years.

The key applications of AI in healthcare include the following. (a) Medical Imaging and Radiology: AI is used in diagnosing diseases and conditions through the analysis of images such as X-rays, CT scans, and MRIs [ 30]. (b) Clinical Trial and Research: AI can streamline the process of clinical trials and research by improving patient selection, monitoring, and data collection [ 31]. (c) Managing Electronic Health Records (EHRs): AI can enhance the management of EHRs, including information capturing, data analysis, and dealing with natural language processing [ 32]. (d) Diagnosis and Clinical Decision Making: AI algorithms significantly contribute to the diagnosis of various diseases, including cancer [ 33], and play a crucial role in clinical decision-making [ 34]. (e) Surgeries: AI-powered robots such as the Da Vinci surgical system and the Smart Tissue Autonomous Robot can perform surgeries with greater precision and accuracy [ 35]. (f) Personalized Medicine: AI may help tailor therapies based on individual genetic information and patient-specific data [ 36], improving the success rates in cancer treatments, for instance [ 37]. (g) Risk Assessment and Prognostication: AI can process historical and real-time data to identify high-risk patients, predict disease progression, and inform clinical decision-making [ 38, 39, 40]. (h) Telemedicine and Virtual Health Assistants: AI can provide assessments and basic healthcare recommendations, improving access to healthcare services [ 41, 42]. (i) Drug Discovery and Development: AI fast-tracks the drug discovery process and can establish effective drug combinations for complex diseases such as cancer [ 43]. (j) Patient Monitoring and Care: AI can monitor patient symptoms in real time, supporting healthcare providers in promptly detecting and addressing complications [ 44]. These applications highlight the potential of AI to significantly enhance the efficiency, accuracy, and precision of healthcare delivery from diagnosis to treatment and follow-up care.

4. The Role of AI in Medicine

In order to fully comprehend the extent of AI's contribution to the healthcare industry, an examination of the existing state of medical care is necessary. This includes acknowledging any limitations that can potentially be addressed and resolved through the integration of AI. For example, when patients lack sufficient understanding of medical procedures it hinders their ability to provide informed consent. This inadequacy is mainly attributed to time constraints and a lack of personalization. To address this issue, the utilization of an AI-powered chatbot has been found to improve the informed consent process. In a study, patients were randomly assigned to either a conventional informed consent group or an AI-supported chatbot group [ 45]. The findings indicated that although satisfaction levels were comparable between the two groups, the AI group exhibited significantly higher levels of accurate comprehension regarding the procedure and its associated risks. The subsequent sections present a comprehensive outline of current challenges within healthcare that stand to benefit from advancements in AI technology.

4.1. Financial Burdens in Healthcare

Excessive spending poses a significant challenge to the US healthcare system. Previous research indicates that unnecessary costs in the range of USD 760 to 935 billion are incurred annually, which represents almost 25% of the country's total healthcare budget [ 46]. This substantial economic burden weighs heavily on the overall US economy and is predominantly due to inefficiencies within the current healthcare system. The integration of AI into this framework has emerged as a potential solution, with projected cost savings of nearly USD 150 billion anticipated by 2026 [ 7]. These extra funds generated through AI implementation could be directed towards addressing existing healthcare concerns, thereby enhancing patient care quality and improving outcomes for patients overall.

Additionally, the application of AI technologies in healthcare can help ease financial burdens in several ways beyond administrative task automation. (a) Improved Diagnosis and Treatment: AI can help improve the accuracy and speed of diagnosis and treatment, which can result in fewer unnecessary tests and procedures, thereby reducing costs. Additionally, predictive analytics can help prevent disease before it occurs, reducing the need for expensive treatments and hospital stays. (b) Telemedicine: AI can support telemedicine platforms in diagnosing patient symptoms remotely. This reduces costs associated with hospital visits and provides a more affordable and accessible option for patients who may not have easy access to a healthcare facility. (c) Drug Discovery: AI can speed up the drug discovery process, which is typically a lengthy and expensive endeavor. By predicting how different drugs will interact with the body, AI can help identify promising drug candidates more quickly, potentially saving significant research and development costs. (d) Personalized Medicine: AI can analyze individual health data to help healthcare providers tailor treatments specifically to the patient's genetic and health profile, potentially increasing treatment efficacy and reducing the need for expensive trial-and-error drug prescribing. (e) Resource Allocation: AI can aid in predicting patient inflow in hospitals and clinics. This helps in better allocation and utilization of resources, reducing wastage and cost overruns. (f) Predictive Maintenance of Equipment: AI can predict when healthcare equipment needs maintenance. Predictive maintenance can help to avoid sudden equipment failures and the costs associated with them, allowing for budgeting of equipment replacement or repair. (g) Insurance Fraud Detection: AI can be used to identify false insurance claims, which can result in significant cost savings for healthcare providers and insurance companies.

4.2. Redundant Administrative Tasks

4.3. Provider Burnout

An increasingly prevalent issue in modern healthcare systems is the phenomenon of provider burnout. Studies have found that burnout is a prevalent issue among healthcare professionals, including physicians and other workers in the field [ 52]. This syndrome can have detrimental effects on mental well-being as well as on the quality and safety of patient care. To mitigate the effects of burnout and improve the overall well-being of healthcare providers, it is crucial to address the contributing factors that lead to burnout, such as excessive workload and burdensome administrative tasks. Provider burnout refers to a chronic stress reaction that arises from emotional exhaustion, mental health decline, and feelings of depersonalization experienced by healthcare professionals due to systemic and organizational factors [ 53]. According to recent research findings, approximately 25.6% of physicians reported experiencing symptoms associated with burnout [ 54]. Interestingly, it was discovered that EHR systems played a significant role in contributing to these symptoms, as indicated by 74.5% of survey respondents attributing EHR as the primary cause [ 54]. Recognizing the critical impact on patient well-being, it becomes essential to acknowledge the importance of maintaining good physical and mental health among healthcare providers who must perform at their utmost capacity to ensure high-quality care delivery. The issue of burnout among healthcare providers remains a persistent challenge for healthcare organizations globally. Efforts are currently underway to address this issue, including the introduction of natural language processing (NLP)-based AI systems [ 55]. These NLP analytics can generate insights and analyze patient data on a large scale, offering opportunities to optimize electronic health record systems cost-effectively and efficiently [ 56]. While integrating NLP in EHR requires continued effort and improvement, the potential benefits of increased efficiency and decreased provider burnout serve as strong motivation for further advancements in this area.

4.4. Diagnosis and Clinical Decision Making

The application of AI in the field of medical diagnosis and treatment is not a new concept. Researchers have been exploring whether AI can accurately suggest clinical diagnoses and recommend optimal treatment plans since the 1970s. The ability to improve the accuracy of clinical diagnoses is seen as an appealing aspect of AI in medicine, as early and precise detection of medical conditions may lead to better patient outcomes by reducing readmission rates, preventing progression to chronic conditions, and improving prognosis with a lower risk for complications. Currently, various subspecialties are showing growing interest in developing advanced AI tools that could assist with clinical problem-solving. These tools can analyze large amounts of medical data, including patient records [ 57], lab results, and imaging data, to identify patterns and correlations that might not be immediately apparent to human clinicians. Thus, the use of AI in clinical decision-making is based on its ability to analyze a vast amount of patient data and identify patterns that can accurately predict clinical outcomes and recommend appropriate treatments. AI algorithms are capable of examining various types of medical information, including radiographic images, laboratory results, and patient records, to assist healthcare professionals in recognizing abnormal patterns indicative of specific diagnoses. Machine learning, an integral component of AI, plays a crucial role in clinical decision-making by analyzing data to make informed decisions and predictions [ 58]. Deep learning (DL), a subfield within ML, has demonstrated considerable success in diagnosing heart failure (HF). In light of the significant global burden posed by HF, which affects approximately 26 million individuals worldwide, the achievements of DL hold great importance in the healthcare context [ 58, 59].

The integration of ML in the field of orthopedics has shown promising outcomes, as AI can provide valuable assistance to orthopedic surgeons by analyzing radiographic images, conducting preoperative risk assessments, and aiding in clinical decision-making [ 60]. Particularly for joint arthroplasty, which is a common elective procedure in orthopedics, AI offers significant advantages over traditional methods. By utilizing advanced vision models, sensors, and feedback mechanisms during robot-assisted arthroplasty surgery with augmented intelligence capabilities, AI technology demonstrates superior performance in achieving precise joint alignment and accurate implant placement tailored to individual patients' needs. This patient-centered approach sets AI-driven augmentation apart from conventional techniques and establishes it as the future direction for arthroplasty procedures [ 61].

AI algorithms have the ability to conduct clinical screenings for disease symptoms, allowing for an assessment of the probability prior to a diagnosis being made by a healthcare professional. One notable example of this is seen in the identification of Diabetic Retinopathy (DR), where several AI screening tools have been created and are now available for use in certain countries. In the past, identifying DR relied solely on human clinicians or graders [ 67]. However, deep learning screening tools can accurately classify individuals with DR at a statistically significant rate of 96.8% [ 68]. By making these automated screening tools accessible to the public, it becomes possible to initiate an appropriate follow-up plan with a clinician much more quickly, thereby reducing the chances of complications resulting from undiagnosed DR.

Numerous endeavors in the past and present have demonstrated AI's capacity to excel in clinical decision-making across various healthcare domains. In these instances, AI has successfully matched the vast knowledge and expertise of healthcare practitioners. However, it is crucial to recognize that the application of AI as both a diagnostic and treatment tool remains in its experimental stages and is not yet widely accessible for use within clinical settings. The widespread integration of AI into healthcare systems remains an additional hurdle on the path toward achieving full automation. The widespread integration of AI in clinical decision-making is expected to occur within the next 5-10 years. However, this timeline relies on obtaining approval from governing stakeholders, standardizing system operations, educating current professionals, and securing adequate funding [ 7]. It is important to note that AI currently does not pose an immediate threat to the job security of healthcare professionals, as their expertise remains crucial for providing the human touch in medicine [ 7]. The development of advanced clinical AI technology aims to enhance physicians' diagnostic abilities and offer optimal treatment plans rather than replace them entirely.

Physical disabilities are increasingly prevalent with advancing age, and rehabilitation plays a crucial role in restoring function and maintaining independence. However, the limited availability and accessibility of rehabilitation services have hindered their clinical impact. While AI has revolutionized several healthcare domains, its potential in rehabilitation remains uncertain. To address this gap, a systematic review of AI-supported physical rehabilitation technology in the clinical setting was conducted in [ 69]. The authors' objectives were to assess the availability of AI-supported physical rehabilitation technology, evaluate its clinical effects, and identify barriers and facilitators to implementation. They identified 28 projects encompassing five categories of AI solutions: app-based systems, robotic devices for function replacement or restoration, gaming systems, and wearables. Among these projects, they analyzed five randomized controlled trials that examined outcomes related to physical function, activity, pain, and health-related quality of life. The results revealed inconsistent clinical effects. Implementation barriers included technology literacy, reliability, and user fatigue, whereas enablers encompassed improved access to rehabilitation programs, remote monitoring of progress, reduced manpower requirements, and lower cost. Hence, while the application of AI in physical rehabilitation is a rapidly growing field, there is a need for more rigorous real-world clinical evaluations and post-implementation experiences in order to fully understand its potential and optimize its benefits. Developers and researchers must strive to conduct robust studies to unlock the true potential of AI in enhancing physical rehabilitation outcomes.

The global prevalence of irritable bowel syndrome (IBS) is approximately 4.1%, leading to decreased quality of life and increased healthcare costs. Current guidelines recommend using symptom-based criteria for diagnosing IBS; however, patients often undergo unnecessary medical interventions. To address this issue, the use of AI in medicine presents a promising solution. This paper aims to review the applications of AI in IBS. AI has proven useful in colonoscopy by detecting organic lesions, diagnosing them, and objectively evaluating the procedure's quality. Additionally, AI has been used to study biofilm characteristics in the large bowel and establish a potential correlation with IBS [ 70]. Furthermore, an AI algorithm has been developed to analyze specific bowel sounds associated with IBS. Smartphone applications based on AI have been created to aid in monitoring IBS symptoms. From a therapeutic perspective, an AI system has been designed to recommend personalized diets based on an individual's microbiota. In conclusion, the future of IBS diagnosis and treatment could greatly benefit from the integration of AI. The era of big data has necessitated the use of AI models to effectively handle the abundance of clinical data available. These data have become invaluable resources for machine learning, with DL models gaining prominence in analyzing unstructured data. However, traditional ML models continue to hold significant potential to enhance healthcare efficiency, especially for structured data. While ML models have been widely applied in predicting diagnoses and prognoses for various diseases, their adoption in gastroenterology has been relatively limited compared to traditional statistical models or DL approaches [ 71].

The prospect of patients reporting their symptoms to AI-powered NLP systems, followed by receiving diagnostic and treatment assistance from these AI systems, represents a promising avenue in healthcare. With the utilization of NLP, patients would have the capability to articulate their symptoms in a conversational manner, providing a comprehensive account of their medical condition. The AI systems would then employ advanced algorithms to process and interpret the information shared by the patients to integrate it with pertinent medical data. This amalgamation of patient-reported data and contextual knowledge would enable the AI systems to generate insightful analyses, potentially aiding physicians in arriving at accurate diagnoses and formulating tailored treatment plans. The integration of AI and NLP in this context has the potential to enhance patient outcomes by streamlining healthcare delivery and empowering healthcare providers with AI-facilitated decision support tools, thereby fostering more efficient and personalized medical care [ 72].

The application of AI in healthcare with potential benefits extending to the field of nutrition assessment has garnered significant attention in recent years. Nutrition assessment plays a pivotal role in healthcare, as it provides crucial insights into an individual's dietary intake, nutritional status, and overall health. Digital technologies such as mobile applications and wearable devices have emerged as valuable tools in facilitating the collection and analysis of dietary data. These technologies enable individuals to track their food consumption, monitor nutritional content, and receive personalized recommendations. AI algorithms enhance the capabilities of these digital tools by utilizing ML and data analytics techniques to process and interpret vast amounts of nutrition-related data. This allows for more accurate and timely assessment of nutritional status, identification of dietary patterns, and evaluation of health risks associated with inadequate or excessive nutrient intake [ 73].

4.5. Surgeries

One of the main challenges in traditional surgery is the lack of uniformity caused by variations in surgical experience, training, and skill levels among physicians. However, advancements such as the above-mentioned STAR have addressed this issue by demonstrating the ability to perform surgeries with greater precision and consistency. The STAR system utilizes three-dimensional techniques that result in more consistent suture spacing and improved resection margins compared to manual procedures. Consequently, this reduces the risk of post-surgical complications arising from variability between surgeons' performances [ 78]. Moreover, predicting surgical outcomes becomes challenging due to inconsistencies in patient anatomy. Nevertheless, STAR tackles this challenge effectively by adapting to inter-patient anatomical differences and tissue deformability during surgery through real-time data processing and regular updates to the surgical plan [ 78]. Advancements made on the development of STAR promise not only enhanced surgical outcomes but also increased predictability and standardization within surgeries.

Surgical procedures are widely acknowledged to pose significant risks to the well-being and safety of patients, primarily due to their unpredictable nature and the potential for human error. The integration of AI into surgical practices has been pursued as a means of addressing these concerns and ensuring enhanced levels of safety. Research indicates that robotic surgery boasts a notably lower mortality rate, with 0.097% compared to manual open surgery's rate of 0.92% per 10,000 surgeries [ 79]. Thus, by complementing physicians' expertise with precise robotic assistance, this technology improves surgical accuracy by approximately 6.4% while reducing surgeons' workload by up to 44% [ 80].

In addition to its applications in the operating room, AI technology has the potential to enhance healthcare outcomes by streamlining surgical decision-making and management both before and after surgery. As surgery is a significant and often anxiety-provoking event for patients, surgeons need to provide comprehensive support and counseling throughout the entire process. Conversely, surgical interventions necessitate extensive preparation that can be time-consuming; aside from performing procedures, surgeons must analyze various datasets such as patient scans and lab work to develop intricate surgical plans. Fortunately, AI offers a solution by efficiently processing large amounts of patient data within a short timeframe while generating optimal solutions. This allows surgeons more opportunity to engage in meaningful interactions with their patients [ 81].

A recent literature review analyzed 46 studies on the applications of AI and ML in spinal surgery [ 82]. The findings revealed that AI/ML models were accurate, with an average overall value of 74.9%. These models performed well in preoperative patient selection, cost prediction, length of stay, functional outcomes, and postoperative mortality prediction. Regression analysis was the most commonly used application, while deep learning/artificial neural networks had the highest sensitivity score of 81.5%. Despite AI/ML's relatively recent adoption, as shown by 77.5% of studies being published after 2018 (depicted in Figure 1), the results have been encouraging. The increasing prevalence of Big Data and National Registries suggests that the field of spine surgery will gradually adopt and integrate AI/ML into clinical practices, leading to significant improvements in patient care.

4.6. Medical Imaging

The introduction of X-ray imaging over 120 years ago revolutionized the way healthcare providers diagnose and treat patients [ 83]. Since then, advancements in imaging technologies have greatly enhanced the medical field by providing a detailed visualization of vital patient anatomy that is not perceptible to the human eye alone. Despite these innovations, modern imaging technology continues to rely on high-quality images and skilled practitioners to accurately interpret and identify potential ailments [ 84]. An important development occurred approximately 50 years ago when AI capabilities expanded beyond data analysis and problem-solving to include image recognition and interpretation with a lower error rate compared to radiologists [ 85, 86]. However, it is crucial to dispel public apprehension, as AI does not aim to replace the expertise of radiology professionals with robotic systems; rather, it complements their skills within this discipline. The integration of AI in radiology has gained significant attention, and is expected to enhance the diagnostic process by supplementing radiologists with AI algorithms. This approach aims to streamline operations, reduce redundancy, and improve accuracy in image interpretation [ 87]. During the COVID-19 pandemic, AI proved invaluable in detecting asymptomatic individuals infected with SARS-CoV-2 on CT scans, allowing radiologists to confidently rule out the disease [ 88, 89]. The use of AI visual recognition tools has played a crucial role in preventing transmission through false negative results. As shown in Figure 4, there has been a noticeable surge of interest among researchers studying the applications of AI technology to medical imaging in recent years [ 90].

AI plays a vital role in automating echocardiographic analysis, aiding in the diagnosis of cardiovascular diseases. A recent literature review discussed AI algorithms used in various stages of analysis, including image acquisition, view classification, cardiac chamber segmentation, and quantification of cardiac structure and function [ 91, 92]. The authors found that such AI models demonstrate high accuracy in comparison to human experts. The same review explored the potential benefits and limitations of AI in healthcare, such as the need for larger datasets and to address algorithm biases to allow for wider clinical adoption.

Endoscopic ultrasound (EUS) has emerged as a widely utilized diagnostic tool for digestive diseases. With the gradual recognition of AI in healthcare, its superiority in the field of EUS has become increasingly evident. Research findings demonstrate that EUS-AI exhibits superiority, or at least equivalence, to conventional methods of diagnosis, prognosis, and quality control for subepithelial lesions, early esophageal cancer, early gastric cancer, and various pancreatic diseases, including pancreatic cystic lesions, autoimmune pancreatitis, and pancreatic cancer [ 93]. The implementation of EUS-AI has opened up new avenues for individualized precision medicine while introducing innovative approaches to diagnosing and treating digestive diseases.

4.7. AI's Role in Pandemics

4.7.1. Diagnosis and Screening

In recent years, significant progress has been made in the development of AI-powered diagnostic tools designed to support healthcare professionals in identifying cases of COVID-19 using medical images. These advanced tools utilize convolutional neural networks (CNNs) to carefully examine chest X-rays and CT scans for potential indications of the virus within lung tissue. By analyzing large datasets containing both infected and non-infected images, these AI algorithms are trained to detect subtle patterns that could suggest the presence of COVID-19. This valuable assistance enables radiologists and clinicians to make faster and more precise diagnoses, particularly in regions where PCR testing (i.e., Polymerase Chain Reaction testing, the "gold standard" for COVID-19 tests) may be limited or subject to delays. The ability of AI systems to swiftly process medical images plays a pivotal role in facilitating early detection and treatment plans, ultimately influencing patient outcomes positively and helping to mitigate the spread of the virus [ 96]. Moreover, AI technology has proven to be beneficial in predicting the progression of COVID-19 cases. By analyzing patterns in symptoms and clinical data, AI algorithms can generate predictive models that help healthcare professionals anticipate how a patient's condition may develop over time. These predictions guide medical professionals in making informed decisions regarding the appropriate course of treatment and allocation of resources.

4.7.2. Drug Discovery and Vaccine Development

The integration of AI has significantly transformed the field of drug discovery. By utilizing AI algorithms, scientists are now able to simulate and predict the interactions between potential drugs and viral proteins. This is achieved through molecular docking, a technique that assesses how a drug molecule binds to its target protein. In addition to molecular docking, AI-enhanced molecular dynamics simulations provide valuable insights into the behavior of drug-protein complexes over time. These simulations aid researchers in selecting promising candidates for further testing by offering detailed information on their stability and efficacy. Moreover, AI models have been instrumental in vaccine development [ 97]. Through advanced prediction techniques, these models can accurately define viral protein structures and analyze potential epitopes that can trigger immune responses. By leveraging such predictions, researchers can design vaccines with greater precision that efficiently elicit strong and protective immune reactions against targeted pathogens. Consequently, this approach has substantially reduced the time required for developing viable vaccine candidates compared to traditional methods. AI has revolutionized the field of drug discovery by enabling researchers to accurately predict drug-protein interactions and efficiently design lead compounds [ 98]. Furthermore, AI has improved the process of drug development by automating various tasks and reducing the cost and time associated with preclinical and clinical trials.

4.7.3. Epidemiological Forecasting

AI-powered epidemiological models incorporate intricate data streams for predicting the transmission of the virus. These models analyze factors such as infection rates, hospitalization rates, population density, and human mobility. Machine learning algorithms play a role in refining these models by adapting them to real-world data. By simulating different scenarios based on varying intervention strategies, these models offer policymakers valuable insights into the potential consequences of their actions. Consequently, they aid in resource allocation decisions, inform the implementation of public health measures, and help to mitigate stress on healthcare systems during periods of high demand. AI-powered epidemiological models have become invaluable tools in predicting the transmission of viruses, including COVID-19. Furthermore, AI-based techniques can be utilized to track and monitor the spread of viruses at different scales, ranging from individual to population levels [ 99].

4.7.4. Remote Monitoring and Telehealth

AI-enabled remote monitoring tools have emerged as a valuable solution for facilitating patient care from the comfort of home. Through the use of wearable devices, vital signs and symptoms can be continuously tracked to generate streams of data. These extensive datasets are then subjected to AI algorithms that accurately identify any deviations from baseline levels, allowing healthcare providers to promptly address potential declines in patients' conditions. Additionally, telehealth platforms employ sophisticated chatbots driven by AI capabilities that aid patients in conducting symptom assessments, providing them with relevant information and determining the urgency required for medical attention. By leveraging these advanced systems, healthcare professionals can effectively enhance patient care while simultaneously mitigating unnecessary face-to-face interactions, an especially crucial measure during times such as widespread pandemics [ 100].

4.7.5. Data Analysis and Decision Support

The COVID-19 pandemic resulted in a significant influx of data, ranging from the number of cases to hospitalizations to genomic sequences. AI systems have played a crucial role in analyzing these datasets to uncover patterns that contribute to decision-making processes. Machine learning algorithms specifically help to identify risk factors correlated with severe outcomes and demographic trends regarding infections and to evaluate the effectiveness of interventions. The insights derived from these analyses can assist public health officials and policymakers in making well-informed choices, effectively allocate resources, and tailor interventions to cater to specific populations [ 101].

4.7.6. Natural Language Processing

Natural language processing techniques facilitate the comprehension and interpretation of human language by AI. In light of the COVID-19 pandemic, NLP tools empowered by AI are capable of systematically sifting through an expansive repertoire of scientific literature to extract significant insights. By summarizing research discoveries and identifying emerging patterns, these advanced tools assist researchers in promptly accessing pertinent information [ 102]. Furthermore, NLP drives the development of chatbots that deliver precise information to the general public, effectively addressing concerns and countering false or misleading narratives [ 103].

4.7.7. Contact Tracing

AI-powered contact tracing applications utilize smartphone technologies such as Bluetooth to monitor and record interactions among individuals. Advanced algorithms powered by machine learning assess the likelihood of disease transmission by considering factors such as proximity, duration, and contextual information [ 104]. To uphold privacy standards, these apps employ decentralized data storage and cryptographic techniques that safeguard sensitive data from unauthorized access. Through the implementation of AI technology, public health agencies can effectively trace potential infection routes, control outbreaks, and promptly notify individuals who may be at risk [ 105]. However, it is important to address privacy concerns that arise with the use of AI-powered contact tracing applications. One way to address privacy concerns in AI-powered contact tracing applications is by incorporating privacy-by-design principles [ 106]. Privacy-by-design principles ensure that privacy is considered at every stage of the design and development process of AI-powered contact tracing applications. By incorporating privacy-by-design principles, contact tracing apps can prioritize user privacy and data protection [ 107]. Additionally, contact tracing apps can adopt a decentralized approach to data storage. This means that personal data are stored locally on users' smartphones rather than being collected and stored centrally. This approach enhances privacy, as it minimizes the risk of unauthorized access to sensitive information. Furthermore, adopting an opt-in system for contact tracing apps can help to address privacy concerns. Adopting an opt-in system for contact tracing apps means that users have the choice to voluntarily participate in the app's functionality. This empowers individuals to make informed decisions about their privacy and control the collection and use of their personal data. Overall, the use of AI-powered contact tracing applications presents significant benefits for public health agencies in controlling disease transmission. However, the collection of personal data and potential surveillance associated with these apps continue to raise valid privacy concerns.

4.7.8. Patient Triage and Resource Allocation

AI-driven triage systems utilize advanced algorithms to assess patient information such as medical history, symptoms, and laboratory findings in order to anticipate the probability of severe illness [ 108]. These machine learning models detect early signs of complications, allowing healthcare professionals to prioritize individuals requiring urgent care. In situations where resources are scarce, AI technologies facilitate the efficient allocation of resources by considering predicted patient outcomes; this ensures optimal utilization of critical supplies like ventilators. In addition to patient triage, AI-based systems play a crucial role in resource allocation within hospitals and on a larger scale. By accurately predicting disease severity and patient outcomes, AI-driven triage systems aid in clinical decision-making as well as in the planning and allocation of resources across hospital systems and at the state/country level [ 109]. Moreover, the use of AI in patient triage and resource allocation becomes particularly vital during pandemics. During the COVID-19 pandemic, AI algorithms demonstrated their effectiveness in predicting patient outcomes and identifying individuals at high risk for severe illness. These AI-powered systems consider a wide range of factors, including pre-existing conditions, laboratory results, and in-hospital data.

4.8. Medical Training

The effectiveness of medical education is closely linked to a doctor's ability to provide proper care for their patients while minimizing the risk of irreversible errors or harm. As a result, physicians undergo extensive training over several years to acquire a well-rounded set of skills encompassing cognitive, psychomotor, and affective domains. In the past, medical education primarily emphasized the retention of large volumes of complex information for long periods. However, this task has become increasingly challenging in light of the vast amount of new knowledge being generated daily, which surpasses human capacity [ 110]. The rapid advancement of AI in the medical field has demonstrated the ability to effectively store, analyze, and retrieve medical data, saving clinicians valuable time and energy in patient management and treatment. This integration of cutting-edge technology necessitates a reformation in the education of future healthcare providers in order to align with these recent advancements, particularly the incorporation of AI into medical practice.

The incorporation of AI-based virtual reality simulations in medical education can offer significant benefits by providing a safer learning environment for students to refine their clinical skills before entering real-world patient care. This approach allows for practice in common scenarios such as suturing or conducting physical examinations while enabling preparation for rare yet potentially impactful catastrophic events. For example, research has demonstrated the feasibility of simulating Operating Rooms using AI technology, which proved effective in analyzing healthcare professionals' response to such emergencies and delivering appropriate training on how to handle these unique situations safely and effectively [ 116]. Furthermore, AI-driven virtual reality simulations have the potential to revolutionize radiology education [ 117]. With the integration of AI intelligence, radiology simulations in virtual environments can reproduce real-world scenarios and provide learners with immersive, interactive, and realistic training experiences that closely mimic clinical practice.

5. Considerations of AI Applications

The integration of AI in the healthcare sector has the potential to greatly enhance patients' overall experience with medical care. As previously mentioned, AI can be implemented across various areas within medicine [ 121]. However, it is important to address certain considerations such as biases and public awareness surrounding AI.

5.1. Ethical and Legal Considerations around AI

There are a number of relevant ethical and legal issues that need to be carefully considered moving forward. (a) Accountability and Liability: as AI systems take on increasingly complex tasks, determining responsibility for mistakes becomes a challenge. If an AI-driven diagnosis leads to harmful medical errors, it is uncertain where the liability rests; is it the healthcare provider, the developers of the AI system, or the machine itself? This legal conundrum needs to be addressed with comprehensive regulations addressing accountability and liability in AI applications in healthcare. (b) Privacy Protection: with AI systems processing vast amounts of sensitive patient information, ensuring data privacy becomes paramount. Patient data can be misused or fall into the wrong hands, making it crucial to have strong data protection measures in place. The legal frameworks regarding data privacy in the context of AI should adhere to principles of data minimization, purpose limitation, and secure data processing methods. (c) Bias and Discrimination: AI systems can unintentionally perpetuate existing biases present in healthcare data, leading to unequal treatment or inaccurate diagnoses for certain groups. Case studies on issues such as racial bias present in healthcare AI algorithms underline the importance of anti-bias measures in the development and deployment of these systems. Regulatory bodies must enforce guidelines that ensure fairness and accuracy in the operation of AI systems. (d) Transparency and Explainability: the 'black-box' nature of many AI systems can significantly complicate healthcare delivery. When an AI system's decision-making process is not transparent, healthcare professionals may find it difficult to trust the system's outputs, affecting implementation. Regulations emphasizing transparency and explainability can facilitate better understanding and acceptance of AI tools in medical practice. (e) Consent: patient consent is another crucial issue when dealing with AI systems. Data used to train AI systems should be collected only after obtaining explicit and informed consent from the patients. Legal requirements need to specify the nature of consent required for different kinds of data usage. (f) Balancing Innovation and Regulatory Oversight: while regulations are necessary to maintain ethical standards, they should not stifle innovation. Policymakers need to ensure that regulatory frameworks provide enough space for the development and application of AI technologies while ensuring patient safety, privacy, and fair treatment.

As the healthcare industry experiences an increased workload resulting from heightened patient demand, digital health technology has garnered more attention as a potential solution. However, this advancement introduces concerns regarding patient privacy and the monitoring of sensitive information. Furthermore, due to the current absence of comprehensive regulations in place, AI systems raise legal and ethical issues such as liability attribution and privacy protection. The challenge lies in identifying responsibility when machines are capable of operating under non-rigid rules and independently acquiring new behavioral patterns [ 122]. This ambiguity poses a legal conundrum, and accountability for these actions remains uncertain. From an ethical standpoint, safeguarding an individual's privacy is closely intertwined with their right to autonomy and personal identity within the medical context. Consequently, maintaining strict confidentiality measures while ensuring robust security protocols for AI-based data management is imperative in order to minimize potential breaches or violations of patient privacy. Moreover, the integration of AI in healthcare raises concerns about algorithmic bias and the potential for discriminatory practices [ 123].

The American Medical Association (AMA) has released seven principles for the use of AI in healthcare [ 129]. The aim is to create a uniform governance framework for the advancement of AI in the industry. The principles stress the importance of comprehensive policies to minimize potential risks associated with AI technologies for patients and physicians. According to the AMA, these principles will aid in optimizing the benefits of AI in healthcare and reducing potential harms. Key aspects within the AMA principles include favoring comprehensive governance for risk management in healthcare AI and promoting transparency with legal requirements in AI design, development, and deployment. In addition, the principles call for privacy-centric AI design, secure handling of personal data, clear disclosure around when AI impacts patient care, and development of policies to address potential negative impacts before deploying generative AI. Furthermore, the AMA urges early identification and mitigation of bias in AI algorithms and advocates for limiting physician liability in the use of AI-enabled technologies while aligning with current medical liability legal frameworks. In the following section, bias and transparency in AI are assessed in more detail.

5.2. Bias and Transparency of AI

Often, AI algorithms are developed and trained using healthcare datasets. When these datasets contain inherent biases, the resulting AI system can replicate or even magnify existing disparities. For example, if a dataset used to train a disease prediction algorithm predominantly contains data from certain demographic groups, the algorithm might perform poorly when used on other demographics. This could lead to unequal treatment or misdiagnosis. Bias in decision making is another concern. AI algorithms often replicate the decision-making processes of the health professionals who trained them. If those professionals carry inherent biases, the AI systems might reflect these same biases. As an instance, a study by Obermeyer et al. found that a healthcare algorithm showed racial bias because it was designed to predict healthcare costs rather than sickness [ 130]. This bias manifested as lower referral rates for black people compared to other racial groups with similar health conditions. To ensure more equitable AI development, efforts must be made to use diverse datasets that accurately represent all population groups. Datasets must be interrogated for potential bias prior to training the algorithms. Furthermore, AI systems should be regularly validated on multiple datasets to ensure that they maintain high performance across diverse populations. AI's capacity in healthcare depends largely on how the ethical issue of algorithmic bias is addressed. Multidisciplinary efforts involving clinicians, data scientists, ethicists, and policy makers should be enforced to ensure the development and use of equitable and unbiased AI systems in healthcare. AI is meant to improve healthcare accessibility and inequality, not to inadvertently exacerbate existing disparities.

Certain diseases have a higher prevalence in specific genders, which presents a challenge when implementing AI for accurate solutions tailored to certain groups. Gender bias represents one of the obstacles encountered during the deployment of AI systems in medical imaging. For instance, a study by Larrazabal et al. revealed that an imbalanced dataset with unequal representation of males and females led to significantly lower performance averages across all diseases within the minority group, suggesting the presence of gender bias [ 131]. This highlights the need to consider gender bias when providing data for AI algorithms and to incorporate additional mechanisms that can effectively account for this bias to ensure precise outputs. It is important to address and mitigate bias in AI algorithms, as it can have negative impacts on health, particularly in under-resourced or racial/ethnic minority populations [ 132].

Obermeyer et al. investigated the potential racial bias present in healthcare AI algorithms used for managing the care of high-risk patients in the United States [ 130]. These algorithms are responsible for setting up special care management programs mandated by the Affordable Care Act designed to provide extra resources to patients with complex medical needs with the aim of preventing costly medical emergencies. Despite race not being an explicit input into the algorithm, the research found significant racial bias in the algorithm's predictions. Black patients were substantially less likely to be selected by the algorithm as needing extra care compared to their White counterparts, even when they were just as sick. This disparity mainly stemmed from the algorithm's focus on health care costs as a proxy for health needs and complexity, which overlooked the fact that less money is spent on Black patients who have the same level of need. The consequent under-identification of high-risk Black patients for care programs has significant implications on the equitable distribution of healthcare resources. These findings suggest the need for careful scrutiny in the design and implementation of healthcare algorithms to ensure that they do not inadvertently propagate systemic biases.

5.3. Cybersecurity

The implementation of EHR systems in hospitals worldwide has led to an increased need for cybersecurity measures to ensure patient safety and protect confidential information. There are two crucial reasons why cybersecurity is imperative in modern healthcare: the digital storage of sensitive patient data, and the vulnerability of electronic systems to remote access by hackers due to inadequate defenses [ 139]. Instances of cybercrime within the healthcare sector include ransom attacks, theft of patient health data, and unauthorized control over implanted medical devices, all of which pose serious risks to individuals' well-being. Additionally, cybersecurity plays a critical role in maintaining privacy and trust on the part of patients [ 140]. One main issue with cybersecurity in healthcare is the abundance of valuable data, including personal and medical information. The other issue is the weak defenses that are typically found in healthcare systems, making them easy targets for cyberattacks. The reliance on electronic health records and other core systems in healthcare means that an effective cybersecurity framework is necessary to protect these systems from threats [ 141]. In contrast to physical breaches, which involve accessing patient records directly, a single medical device can serve as an entry point into the health record software of any hospital [ 142]. The use of AI introduces new opportunities for attackers to compromise patient privacy and undermine healthcare security, which is already vulnerable; therefore, it is crucial to develop legislation that protects patient information and to implement effective digital safeguards before the widespread adoption of AI algorithms in medicine [ 143]. Fortunately, there is potential for AI itself to contribute to maintaining patient privacy through computational text de-identification algorithms, which may offer a faster and more cost-effective solution compared to human experts [ 144].

5.4. Public Opinion around AI in Healthcare

The implementation of AI in healthcare is progressing rapidly, and it is crucial to consider public opinion on its integration. While there are vast possibilities for AI to enhance the healthcare sector, it is important to acknowledge that public perception can significantly impact how AI technologies are adopted. This includes the public perspective on various aspects, such as data collection during early stages of medical care, as well as acceptance and trust in using AI for diagnoses and treatments later on [ 145]. It is clear that the public's perception of AI in healthcare is not homogeneous and that there are varying levels of acceptance and apprehension. Recent surveys conducted by Pew Research have assessed public sentiment across multiple categories, including reliance on and impact of AI in medical care, knowledge about diagnosis and treatment methods, utilization of surgical procedures and pain management techniques, and the use of chatbots for mental health support [ 146]. In general, the majority of individuals express discomfort with providers relying solely on AI for medical care. There is a divide in opinion regarding whether this reliance improves, worsens, or has no significant impact on overall healthcare outcomes. The public's perception of AI in healthcare is influenced by factors such as education, knowledge about AI, and previous experiences [ 147]. Similarly, the findings presented in Figure 5 indicate that individuals with higher education and knowledge about AI tend to have greater confidence in its capacity to enhance patient outcomes within the healthcare sector. Hence, individuals with higher education tend to perceive AI as a valuable tool in enhancing patient care, facilitating early detection and diagnosis, optimizing treatment plans, and improving overall healthcare outcomes. However, according to the survey findings the majority of respondents expressed reservations about using AI for determining pain medication after surgery or relying on surgical robots for procedures. Furthermore, most participants believed that incorporating AI into pain management would either exacerbate their symptoms or yield no significant differences. Similarly, a significant majority indicated an unwillingness to utilize an AI chatbot for mental health support unless they were concurrently seeing a therapist [ 148]. These opinions held true both among those familiar with chatbots and those who had no previous knowledge of them. Overall, this survey found that while there is an increasing awareness and interest in AI's potential in healthcare, there was also skepticism and concern among the public. This skepticism and concern may stem from a lack of understanding or familiarity with AI as a concept as well as apprehension about data privacy and control [ 146]. To address this, it is important to conduct comprehensive research that takes into account the public's perspective from various qualitative and quantitative studies, even though public perceptions of AI in healthcare are not limited to concerns about trust and reliance on technology.

The survey findings indicate a strong association between knowledge of AI and belief in its potential to have a positive impact on healthcare. Participants who demonstrated a higher level of understanding were more inclined to embrace the integration of AI into their overall medical treatment. However, this correlation weakened when it came to specific areas such as mental health, where individuals may have been less receptive towards incorporating AI into their treatment for these conditions. Nevertheless, AI has diverse applications in mental healthcare beyond the scope of chatbots alone. While patients may show decreased interest in using AI for treating their mental health issues, it can offer benefits for diagnostic purposes. For example, AI-based speech analysis techniques have been developed to identify depression [ 149] and wearable AI devices have the potential to detect anxiety, although their current level of advancement does not yet allow for clinical use [ 150].

The surveys reveal an overarching lack of awareness among the general public regarding AI in healthcare settings, emphasizing the necessity for targeted efforts aimed at educating and informing individuals about its capabilities within this domain. Addressing this knowledge gap is crucial for fostering effective collaboration between AI initiatives and healthcare practices in order to enhance patient care outcomes and optimize the use of resources in the healthcare system. Additionally, it can be inferred that healthcare specialists who have a deeper understanding of AI are more inclined to embrace and utilize this technology in their professional roles to enhance patient care.

While the general sentiment towards the utilization of AI in healthcare remains predominantly positive, it is crucial to acknowledge and address certain prevailing challenges that demand increased attention. Merely focusing on algorithmic concerns is insufficient; it is imperative to delve deeper into the practical implementation of legislation and guidelines to ensure the incorporation of principles such as fairness, accountability, transparency, and ethics within AI systems deployed in healthcare settings [ 151]. This comprehensive approach will contribute to safeguarding the integrity and responsible use of AI technology in healthcare.

6. Discussion

As the digital revolution continues to accelerate, it is clear that AI is becoming increasingly integral to healthcare. Its benefits, which include enhancing diagnostic accuracy, personalizing treatments, and increasing operational efficiency, have the potential to profoundly improve patient outcomes and reduce the burden on healthcare systems around the world. However, it is important to acknowledge that successful integration of AI into healthcare practices demands more than just technical proficiency. The ethical and social implications of AI usage in healthcare present considerable challenges. There is an urgent need to address issues such as data privacy, algorithmic bias, and accountability. Ensuring fairness, reliability, and transparency should be a priority in AI development. Moreover, collaboration between AI specialists, healthcare professionals, policy makers, and patients is key to developing AI tools that can truly meet the needs of all stakeholders. Additionally, due to the complex nature of medical decision-making, the role of AI should be envisioned as a support tool for clinicians rather than a replacement for human judgment. The human element in healthcare, which encompasses empathy, complex reasoning, and understanding of individual patient needs, cannot be replicated by an algorithm. Lastly, it is necessary to shift focus towards education and public awareness about AI in healthcare. Encouraging public understanding and dialogue about AI will be crucial for building trust and acceptance in the system. With a balanced and thoughtful approach, AI has the capacity to transform healthcare, bringing enormous benefits to both patients and providers. The rapidly evolving field of AI in healthcare will continue to offer exciting opportunities for further exploration and discovery in future work.

The field of AI has experienced rapid advancements and is now being utilized across various industries. Originally designed as a large computer for deciphering war signals, AI has evolved into a sophisticated cloud-based neural network. It functions as an adaptable tool that enhances complex human tasks in different sectors. In the medical domain specifically, the implementation of AI shows immense potential for future applications [ 152]. By demonstrating proficiency in surgical procedures, administrative responsibilities, diagnostics, imaging techniques, medical education, and patient management, AI can significantly alleviate healthcare burdens while fostering greater efficiency. This collaborative approach ensures that AI algorithms work alongside clinicians to enhance health outcomes and quality of care without posing any threat to job security or replacing human expertise in the healthcare field.

Collaboration among clinicians, educators, researchers, and developers is essential for the successful integration of AI into healthcare in the coming decade. Despite the impressive advancements made by AI, there remain many important considerations that need to be addressed. These include addressing biases and ensuring transparency as well as tackling legal and ethical challenges. Additionally, it is imperative to take into account public opinion and awareness regarding the incorporation of AI in medicine, as this will greatly influence progress within the field. Nonetheless, despite the need for these challenges to be resolved over time, AI has already demonstrated numerous promising capabilities that have tremendous potential for improving healthcare delivery and shaping the future of medicine. It should be emphasized that while AI shows immense promise, it necessitates rigorous validation processes along with ongoing collaboration between experts in AI and medical professionals to ensure safe implementation practices yielding effective results.

In light of the aforementioned challenges associated with AI in healthcare, the AMA has introduced guidelines pertaining to the advancement and application of AI in the healthcare sector. These guidelines aim to tackle critical concerns, including data privacy, cybersecurity, and the utilization of AI by insurance providers [ 129]. The report emphasizes the "quadruple aim" for AI systems: to enhance patient care and outcomes, improve population health, reduce healthcare costs while increasing value, and support the wellbeing of healthcare professionals. The guidelines further stress the need for regulatory oversight, transparency, data protection, and the avoidance of exacerbating inequities in healthcare while aiming to ensure AI tools meet both physician and patient needs.

7. Conclusions

An essential point to underscore is the collaborative potential between AI and healthcare professionals. AI is best viewed not as a replacement for healthcare professionals but as an adjunct tool, that is, a partner that can handle extensive data analysis, provide diagnostic support, and free up more time for healthcare professionals to spend on direct patient care. The successful implementation of AI would enable healthcare providers to leverage their unique skills of empathy, complex decision-making, and direct patient communication that AI cannot emulate. Rather than eliminating jobs, AI is more likely to transform them, shifting healthcare professionals' emphasis to duties that complement AI technologies. Nevertheless, it is worth noting that along with the exciting advancements and numerous advantages there are challenges that need to be tackled. Key among these are the legal and ethical complexities inherent in AI's integration into healthcare. Moving forward, there is significant potential for AI in healthcare; however, realizing this potential will require continued research, cross-disciplinary cooperation, and open dialogue among AI developers, healthcare professionals, and ethical and legal experts. Combining AI's capabilities with the human elements of healthcare professionals will lead to more personalized, effective, and efficient patient care.

Author Contributions

Conceptualization, M.B., J.M., C.W.O. and M.T.; methodology, M.B., J.M., C.W.O. and M.T.; formal analysis, M.B., J.M., C.W.O. and M.T.; investigation, M.B., J.M., C.W.O. and M.T.; resources, M.B., J.M., C.W.O. and M.T.; data curation, M.B., J.M., C.W.O. and M.T.; writing-original draft preparation, M.B., J.M., C.W.O. and M.T.; writing-review and editing, M.B., J.M., C.W.O. and M.T.; visualization, M.B., J.M., C.W.O. and M.T.; supervision, M.B., J.M., C.W.O. and M.T.; project administration, M.B., J.M., C.W.O. and M.T.; funding acquisition, M.B., J.M., C.W.O. and M.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

The authors declare no conflicts of interest.

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Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

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Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data.

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1. Introduction

In more detail, AI comprises any system with the ability to sense, reason, engage, and learn that can be used for various human-like functions, such as understanding digital images, voice recognition, motion, planning, and organization. On the other hand, machine learning (ML) is a subset of AI that uses statistical techniques to endow computer systems with the ability to improve with experience. Specifically, ML encompasses the AI tools that can adapt their models to improve predictions, leading to a progressive performance enhancement for the set task. Theoretically, ML methods can be applied to datasets of any size, yet a larger amount of data provides more experience for training the model. The working principle of ML assumes feeding these features into computational models that can offer insights into the observations, such as clustering similar observations into groups or predicting certain outcomes. Moving further, deep learning (DL) assumes the subclass of ML where algorithms can train themselves due to the "self-learning" ability achieved through a sequential chain of pivotal features from input data. Data representations are automatically mastered by deep neural networks (DNNs) that can learn very complex nonlinear mathematical functions. The term "deep" is used in reference to the number of layers (so-called neurons) or iterations between the input and output. In more detail, input features are fed into the first layer of neurons and propagated towards the output layer, the process being inspired by the information processing principles of biological neurons [ 1, 7, 8, 9]. The relationship between AI, ML, and DL is visually represented in Figure 1. Moreover, Table 1 briefly defines the most important and relevant concepts concerning AI tools.

In recent years, AI has attracted more and more research interest in medicine, being investigated for a plethora of applications. Numerous studies have evaluated various aspects of the healthcare system, reporting the progress in AI involvement in the prevention, screening, and treatment of diseases and prediction of the prognosis [ 14, 15].

In this context, the present paper thoroughly reviews the most recent potential clinical applications of AI in the fields of cardiology, neurology, oncology, hematology, nephrology, gastroenterology, hepatology, orthopedics, and rheumatology, further focusing on the challenges that limit their introduction to clinical practice. More specifically, this work reviews English language research articles in the fields published in the last 5 years (2018-2022). Studies were retrieved from the Science Direct and Google Scholar databases using combinations between the following keywords: "artificial intelligence", "machine learning", "deep learning", "clinical applications", "prediction", "diagnosis", "screening", "treatment", "prognosis", "cardiology", "neurology", "oncology", "cancer", "hematology", "nephrology", "gastroenterology", "hepatology", "orthopedics", and "rheumatology". The relevant search results were manually selected; the papers were analyzed and discussed in the main text of the review; and the studies for which at least one of the following performance metrics was available, namely accuracy, precision, sensitivity, specificity, or correlation between the automated and manual measurements, were additionally summarized in a series of tables. The choice of the included medical specialties was based on the available literature data, as other areas were not as explored in recent years or remained at the level of hypothesis/opinion papers.

2. Applications of AI in Clinical Care

Artificial intelligence has faced tremendous advances in recent years, rendering it interesting for applications in a variety of clinical care procedures ( Figure 2). In this respect, the following subsections discuss the most recent studies concerning AI involvement in medicine, classified according to medical specialties. Moreover, the studies for which at least one of the following performance metrics was available, namely accuracy, precision, sensitivity, specificity, or correlations between automated and manual measurements, are included in the summative tables.

2.1. Cardiology

Cardiovascular diseases represent one of the leading causes of morbidity and mortality worldwide, requiring expensive treatments and posing a burden on both patients and the healthcare system [ 16]. Introducing AI technology to the field of cardiology holds great promise for improving the prediction and diagnosis of cardiac events and visualizing cardiac anomalies that anticipate patients' needs and provide personalized medical care. This is particularly appealing in cardiology due to the large amount and variety of available biological data. By properly analyzing and interpreting images, pulse waves, electrocardiograms, and sound information, various algorithms can identify patterns that lead to disease offset or aggravation, helping the cardiologists in choosing the best treatment alternative [ 15, 17].

For instance, Ye et al. [ 18] developed and validated a model for predicting incident essential hypertension. The authors used a machine learning algorithm for processing electronic health record (EHR) data that generated an ensemble of classification trees and assigned a predictive risk score to each individual. As the model was able to accurately predict incident essential hypertension for the following year, it has been deployed in the state of Maine to provide implications in interventions for hypertension and associated diseases and, subsequentially, improve hypertension care.

Another interesting example was proposed by Tison and colleagues [ 19], who created a deep neural network for detecting atrial fibrillation using smartwatch data, such as heart rate and step count. The deep neural network (DNN) was trained by the heuristic pretraining method (the network approximated representations of the R-R interval (i.e., the time between heartbeats) without manual labeling of the training data) and validated both against the reference standard 12-lead electrocardiography in a separate cohort of patients undergoing cardioversion and on smartwatch data from ambulatory patients against the reference standard of self-reported persistent AF history. The authors concluded that the combination of smartwatch photoplethysmography and DNN could be a solution for passively detecting AF, with some reduction in sensitivity and specificity against a criterion standard ECG.

In contrast, Picon et al. [ 20] coupled one-dimensional convolutional neural network (1D-CNN) layers and a long short-term memory (LSTM) network into a deep learning architecture for detecting ventricular fibrillation. The newly proposed DL architecture was compared to 1D-CNN only and to a classical approach based on ventricular fibrillation (VF) detection features and a support vector machine (SVM), outperforming these classifiers. Up to that moment, and according to the authors' knowledge, this algorithm was the most accurate for VF detection, with the potential to enable an accurate shock or no shock diagnosis in a very short time.

Artificial intelligence was also found to be useful for the classification of aortic stenosis, as demonstrated by Yang et al. [ 21]. The research group used a feature analysis framework to reduce the features from collected cardio-mechanical signals generated by continuous wavelet transform. Several ML algorithms were compared by performance, while an additional 2D-CNN was developed using CWT coefficients as images. The obtained accuracies validated the effectiveness of the feature selection and classification framework, encouraging the implementation of AI tools in AS classification.

Alternatively, Eberhard et al. [ 22] evaluated the feasibility of a computed tomography-derived fractional flow reserve (FFR CT) in patients presenting to the emergency department with acute chest pain who underwent chest pain-computed tomography (CPCT). As FFR CT allows a noninvasive functional assessment of coronary artery stenosis, involving ML-based software for performing the measurements could only make it more advantageous. By evaluating the agreement between the results from the FFR CT and patient outcomes for a follow-up of three months, the authors concluded that this method was feasible for the tested category of patients. Therefore, this approach can be implemented in clinical care for improving patient triage by reducing the need for further downstream testing. Nonetheless, some limitations were noticed in patients with CT signs of acute plaque rupture, so further research is needed.

Another study conducted by Nguyen et al. [ 23] proposed an algorithm based on a CNN as a feature extractor and a Boosting classifier for detecting sudden cardiac arrest on an electrocardiogram signal. Their developed shock advice algorithm applied in the automated external defibrillator showed a validated performance, with the accuracy, sensitivity, and specificity above the preexisting algorithms for the same task. Thus, its high and reliable detection represents a potential asset in clinical settings, the correct detection of SCA being essential for improving the survival rate and reducing unnecessary defibrillation.

Retson and colleagues [ 24] developed a deep learning algorithm for the clinical measurements of the right and left ventricular volumes and functions. MRI images for various clinical indications and pathologies were analyzed with the aid of commercially available software for the automated DL-based and manual contouring of biventricular volumes. Given the promising obtained results, the authors concluded that their algorithm could be used to aid in expert segmentation; however, the model can benefit from expert supervision, especially for solving errors of the basal and apical slices.

Encouraging results were also obtained by Hannun et al. [ 25], who developed a DNN able to classify 12 rhythm classes based on single-lead electrocardiograms. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the DNN performance outperformed the specialists, exceeding the average cardiologist sensitivity for all the rhythm classes. Hence, it may be expected that introducing this DL-based approach into the clinical setting could lower the rate of misdiagnosed computerized ECG interpretations and enhance the human ECG interpretation efficiency by properly triaging patients and prioritizing the most urgent conditions.

To summarize the discussion in a clear and easy-to-follow manner, Table 2 comprises the information regarding the objectives of the studies mentioned above, the used AI approaches, the data sources for the developed algorithms, and the performance metrics.

2.2. Neurology

The benefits of AI have also attracted attention in the area of clinical neurosciences, as newly developed tools can ensure early detection and improve the management of neurological conditions [ 26].

Abedi et al. [ 27] recently investigated ML-based tools for predicting stroke recurrence and identifying the key variables. All the selected algorithms (i.e., logistic regression, XGBoost, gradient boosting machine, random forest, SVM, and decision tree) could be trained to predict the occurrence of long-term ischemic stroke, and laboratory-based variables were highly correlated with stroke recurrence, paving the way for personalized interventions. Another study on acute ischemic stroke was conducted by Rava et al. [ 28], who researched the matter from the perspective of collateral circulation. AI-based algorithms can accurately and efficiently determine a patient's degree of collateral flow, being a potential tool for helping in the decision of which patients are eligible for a reperfusion procedure.

Tackling the potential of machine learning as well, Young et al. [ 29] introduced a ML technique called "Subtype and Stage Interference (SuStaIn)". SuStaIn can be used for identifying genotypes from imaging alone in neurodegenerative diseases, such as genetic frontotemporal dementia and Alzheimer's disease. Providing fine-grained patient stratification, SuStaIn can significantly enhance the ability to predict the conversion between diagnostic categories over standard models that ignore the subtype or temporal stage. Thus, it holds great promise in enabling disease subtype discovery and precision medicine. Following similar considerations, Eshaghi and colleagues [ 30] applied unsupervised ML to brain MRI scans to classify multiple sclerosis based on the pathological features. According to the authors' findings, the identified subtypes predicted the disability progression and patients' responses to treatment, SuStaIn being a valuable technique for use in defining groups of patients in interventional studies.

Considerable research interest has been invested in applying AI technology to detecting epilepsy and managing this disease. For instance, Jin et al. [ 31] coupled machine learning with automated surface-based MRI morphometry, obtaining a robust performance of detecting FCD during presurgical evaluations for patients with pharmacoresistant epilepsy. Using a similar approach, Gleichgerrcht et al. [ 32] focused on improving the detection of brain abnormalities in temporal lobe epilepsy patients, concluding that ML has the potential to aid in the radiological diagnosis of this disease. Alternatively, Daoud and Bayoumi [ 33] developed a deep learning-based technique applied to long-term scalp electroencephalogram recordings for predicting incoming epileptic seizures, obtaining the best performance among the state-of-the-art techniques. As recently demonstrated by Quon and colleagues [ 34], DL also represents a potential solution in automatically classifying intracranial epileptiform discharges (IEDs). Using a template-matching algorithm and a CNN, the authors obtained comparable performances to expert clinical neurophysiologists, confident in their study's potential practical applications.

Another interesting study for the field of neurology was reported by Qiu et al. [ 35]. The authors proposed an interpretable DL strategy for the delineation of unique Alzheimer's disease signatures from multimodal inputs. A fully convolutional network was involved for constructing high-resolution maps of disease probability from the local brain structure to a multilayer perceptron. In this manner, the model could generate precise, intuitive visualization of an individual's Alzheimer's disease risk, identifying nuanced neuroimaging signatures for diagnosing this condition.

Research has also been conducted towards improving the diagnosis and care of other neurodegenerative disorders. For example, Shinde et al. [ 36] established a computer-based analysis technique to create prognostic and diagnostic biomarkers of Parkinson's disease (PD) by the use of neuromelanin-sensitive MRI (NMS-MRI). NMS-MRI has been involved, as this method can help identify the abnormalities in the substantia nigra pars compacta (SNc) in PD, since condition is characterized by the loss of dopaminergic neurons in the SNc. The proposed CNN-based method offered a testing accuracy superior to contrast ratio-based classification and the radiomics classifier, supporting PD discrimination from atypical parkinsonian syndromes. The authors concluded that their technique might support the radiological diagnosis of PD while facilitating a deeper understanding of the abnormalities in SNc.

Important results were also reported in a study performed on patients in the intensive care unit who had an acute brain injury and were unresponsive to spoken commands. Claassen et al. [ 37] applied ML to patients' EEG recordings to detect brain activation in response to commands that patients move their hands. The authors observed in this manner that, early after brain injury, 15% of the clinically unresponsive patients who did not follow spoken motor commands presented EEG evidence of brain activation in response to them.

Artificial intelligence can also be involved in improving brain surgeries. Shahjouei et al. [ 38] developed an ANN for predicting the safe clipping time (SCT) of temporary artery occlusion during intracranial aneurysm surgery. The proposed technique works offline, estimating the SCT before the surgery; however, the authors suggested that an online version would provide a more accurate and precise SCT during the surgery.

A summary of the above-presented studies is comprised in Table 3.

In addition to neurological condition-specific studies, interest has also been drawn to integrating AI algorithms into neurosurgical audits. Brzezicki et al. [ 39] used the Frideswide algorithm to analyze the same dataset (clinical notes of 45 medical outliers on a neurosurgical ward) as 46 human students. The AI-based algorithm produced considerably more recommendations in a shorter time, the audits being more factually accurate and logically consistent. Thus, this method may help improve the safety, efficiency, and quality of care, implying only a small part of the resources would be required to conduct it through human processes.

2.3. Oncology

Cancer comprises a group of more than a hundred types of diseases characterized by abnormal cell growth in different body parts and requires prompt and adequate treatment to prevent serious health issues and increase patients' survival rates [ 40, 41, 42, 43]. As cancer poses tremendous burdens on patients and healthcare systems worldwide, there is no surprise that AI has started being investigated in relation to oncology. AI may assist with collecting and evaluating data, diagnose the information on the basis of health, match it with prior information and expertise, and choose adequate diagnostic treatment plans. Thus, it has been studied for improving the diagnosis and management of many forms of cancer, including breast, lung, thyroid, oral, gastric, colorectal, liver, and skin cancers [ 44].

For instance, Rocca et al. [ 45] used AI models for improving the radiological diagnosis of colorectal cancer liver metastases (CRCLM). With a precision of 100%, Formal Methods (FM) can help medical professionals predict the presence of liver metastasis still undetectable when using the standard protocols. Thus, the authors concluded that FM could effectively detect CRCLM, even in very heterogeneous and small clinical samples.

Alternatively, Lu and colleagues [ 46] employed the Faster RCNN model to create a recognition framework for colorectal cancer tumor sprouting. The model can automatically identify the budding areas from pathological sections and count their numbers in a short time, with high accuracy. Therefore, this method can improve the diagnostic efficacy while also reducing the burden of pathologists in reading the sections.

Lee et al. [ 47] investigated the use of AI in analyzing pancreatic cancer recurrence after surgery and its determinants. Employing the random forest algorithm, the authors concluded that the major predictors of disease-free survival were tumor size, tumor grade, TNM stage, T stage, and lymphovascular invasion. Thus, this technology represents a promising decision support system for treating patients undergoing surgery for pancreatic cancer; yet, further studies are required to demonstrate its benefits in clinical practice.

A different study, conducted by Pantanowitz et al. [ 48], focused on aiding in a prostate cancer diagnosis from H&E-stained slides of prostate core needle biopsies. Their computer-assisted diagnostic tool could accurately detect, grade, and evaluate clinically relevant features in digitized slides. These encouraging results suggest that the developed technique could be a useful asset in automating the screening of prostate biopsies for a primary diagnosis, assessing signed-out cases for quality control purposes, and standardizing reporting.

An interesting AI approach for cancer care was proposed by Faron et al. [ 49]. The authors used an automated DL-based body composition analysis pipeline to predict the outcome in patients with melanoma receiving immune checkpoint inhibitor therapy. The model identified a lowered skeletal muscle mass as an independent predictor of mortality, the patients with such characteristics displaying increased mortality rates up to three years after starting the treatment.

An innovative ML-based strategy for diagnosing laryngeal cancer was reported by Kim et al. [ 50]. The researchers investigated if an automated voice signal analysis could help to discriminate between patients with laryngeal cancer and healthy individuals. Promising results were obtained, as the deployed method demonstrated a greater performance than trained laryngologists in identifying the diseased.

Recent research was also directed at developing a reliable tool for estimating radiation doses before any planning of head and neck radiation therapy. In this respect, Chan et al. [ 51] created a machine learning-based clinical decision support system that could predict whether mandible subsites would receive a mean dose higher than 50 Gy. Obtaining promising results, the authors concluded that the implementation of such a dose prediction system would allow for more precise estimations of radiation side effects in specific at-risk organs.

One more study that focused on cancer therapy rather than diagnosis/monitoring was proposed by Houy and Le Grand [ 52]. The authors used AI technology for computing optimal personalized protocols for temozolomide administration in a heterogeneous population of patients. Every day, the protocol was updated with the feedback from the patients' reactions to drug administration, resulting in very different personalized protocols between the tested group and the standard maximum tolerated dose protocol. This treatment customization was reflected in a reduced tumor size, on average and patient-wise, while avoiding severe toxicity.

To summarize the discussion on the oncological applications of AI, Table 4 was created.

2.4. Hematology

AI approaches have also gathered interest in benign and malign hematology settings, being researched for applications in the diagnosis and prognosis of various forms of leukemia, lymphoma, anemias, and genetic blood disorders [ 53, 54].

Carreras et al. [ 55] recently employed an algorithm using multilayer perceptron ANN to highlight new markers and predict the overall survival of patients with mantle cell lymphoma (MCL) (a subtype of mature B-cell non-Hodgkin's lymphoma). The AI tool identified five genes associated with poor survival (i.e., KIF18A, YBX3, PEMT, GCNA, and POGLUT3) and five genes linked with favorable survival (i.e., SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2). By the further use of several ML-based algorithms, the authors obtained high accuracy predictions of the overall survival of MCL patients.

In another recent study, El Hussein et al. [ 56] investigated the use of a novel AI-based heatmap technique for the objective assessment of proliferation centers in chronic lymphocytic leukemia (CLL). The integrative analysis of the cell nuclear size and mean nuclear intensity model demonstrated a high accuracy in separating the three progression stages, displaying robust diagnosis predictive values.

Alternatively, Boldú et al. [ 57] used peripheral blood cell images to predict an acute leukemia diagnosis. The authors used a DL-based approach, configuring a system with two sequentially working CNN modules: the first one for recognizing abnormal promyelocytes among other mononuclear blood cell images, and the second module for distinguishing whether the blasts were of myeloid or lymphoid lineage. The as-designed model represents a promising asset for clinical pathologists, helping them diagnose acute leukemia during a blood smear review. In comparison, Didi and colleagues [ 58] trained and compared ML and DL predictive models in order to predict the best treatment for newly diagnosed acute myeloid leukemia. The AI algorithms outperformed the classical statistical analysis or naïve predictors, also predicting the overall survival with high accuracy.

A study proposed by AlAgha et al. [ 59] focused on overcoming the challenges in a thalassemia diagnosis. Using simple laboratory test results, the authors involved a hybrid data mining approach to differentiate between healthy individuals and persons carrying beta-thalassemia. In addition to the promising performance of the proposed model, this method also helped to reduce the diagnosis cost and time as, without AI tools, additional tests would have been needed to correctly identify the diseased persons.

A different study, conducted by Memmolo et al. [ 60], aimed to find better solutions for the early differential diagnosis of anemia. The authors used label-free holographic microscopy coupled with a hierarchical ML decider, obtaining enough accuracy for discerning between different hereditary anemia classes with minimal morphological differences. In addition, the method only requires a fraction of a blood drop, reducing the necessary volume of blood drawn for a correct diagnosis. Moreover, this study opened the door for point-of-care blood testing and telemedicine with lab-on-chip platforms.

Given the significant interindividual variabilities of presentation and the clinical course among patients with sickle cell anemia, Dutra et al. [ 61] proposed an AI method for better understanding this disease. By using a cluster analysis, the authors identified five clusters differentiated by unconjugated bilirubin, reticulocytes, lactate dehydrogenase, leukocytes, lymphocytes, and monocytes. Furthermore, it was investigated if this grouping could be correlated with clinical manifestations, concluding that the clusters exhibited different degrees of inflammation, hemolysis, and liver abnormalities.

Research has also been conducted towards improving anemia management in hemodialysis patients. Barbieri et al. [ 62] developed an AI decision support system called the "Anemia Control Model" (ACM) that can recommend suitable erythropoietic-stimulating agent doses according to patient profiles. ACM showed promising results, as it helped improve anemia outcomes in the target population while minimizing their medication use and reducing the cost of treatment.

As cytomorphology represents the gold standard for assessing peripheral blood and bone marrow samples in hematological neoplasms, interesting investigations have also been performed for enhancing this method through AI tools. In this respect, Haferlach et al. [ 63] conducted a prospective, blinded clinical study (NCT04466059) in which they compared a blood smears analysis done by an ML-based model vs. routine diagnostics, obtaining a correlation of 95% for the pathogenic cases. Alternatively, Osman et al. [ 64] tackled the benefits of a CNN for separating monocytes from their precursor cells (i.e., promonocytes and monoblasts). The authors concluded that the AI-based method could reach an accuracy comparable to the human reviewers, suggesting that CNN models could be used for this task and further improved with a larger study population.

The studies discussed above are outlined in Table 5.

2.5. Nephrology

The advancement of digitalization and widespread availability of EHR have been reflected in finding better solutions of care in nephrology settings. AI influence has been specially investigated for the early detection and prediction of acute kidney injury (AKI) in an effort to help clinicians intervene during what may be a crucial stage for preventing permanent kidney injury [ 65, 66].

For example, Tomašev et al. [ 67] developed a DL approach involving an RNN for the continuous risk prediction of future deterioration in patients with AKI. This method may ensure the identification of patients at risk within a time window that would enable early treatment and, consequently, improve the outcomes.

In contrast, other scientists have employed several ML algorithms for better tackling AKI recognition and prediction. Specifically, Mohamadlou et al. [ 65] used XGBoost to train an AKI prediction tool on retrospective data. The promising results obtained by the authors encouraged them to state that this method may provide important prognostic capabilities for determining which patients are likely to suffer AKI, but, being a retrospective study, no conclusions could be drawn about the impact on patient outcomes in a clinical setting. Alternatively, Adhikari et al. [ 66] proposed an algorithm, called "Intraoperative Data Embedded Analytics" (IDEA), to readjust the preoperative risk by the use of a physiological time series and other data collected during surgery. The dynamic incorporation of intraoperative data resulted in improving the postoperative AKI predictions with high sensitivity and specificity. ML algorithms also proved useful in recognizing AKI in burn patients, as demonstrated through studies conducted by Tran et al. [ 68] and Lin et al. [ 69].

Another category of nephrological conditions that gathered attention for AI utilization is represented by chronic kidney disease (CKD) in its various forms, including immunoglobulin A nephropathy (IgAN), diabetic kidney diseases (DKD), and autosomal dominant polycystic kidney disease (ADPKD) [ 3].

For instance, Chen et al. [ 70] used the XGBoost algorithm coupled with a survival analysis to stratify the risk for kidney disease progression in the setting of IgAN. By using routinely available characteristics, such as urine protein excretion, global sclerosis, and tubular atrophy/interstitial fibrosis, the model could accurately predict the outcome. In comparison, Schena et al. [ 71] recently developed an ANN prediction model for end-stage kidney disease in patients with IgAN. Being a retrospective study, the authors could compare the predicted and observed outcomes, reporting similar results over a 25-year follow-up period.

A distinct study conducted by Makino and colleagues [ 72] focused on better understanding DKD. The authors developed a predictive model for DKD aggravation by the use of AI, processing natural language and longitudinal data with big data machine learning. The proposed model may assist in a more effective and accurate intervention towards reducing hemodialysis.

AI can also be employed for automatically determining an estimated glomerular filtration rate (eGFR) and CKD status. In this respect, Kuo et al. [ 73] used a CNN, called ResNet, to predict kidney function based on ultrasound images, obtaining good performance for the model. Alternatively, Li et al. [ 74] exploited an ANN integrated with more independent variables for developing an accurate GFR estimation model for the Chinese population. The authors suggested that such a model could fully utilize the predictive ability of additional auxiliary variables but requires further validation in more diverse cohort data.

The potential of AI has also been investigated for predicting dry weight in hemodialysis patients [ 75], predicting the calciphylaxis risk and understanding the disease mechanism [ 76], analyzing histopathological images towards improving the diagnostic accuracy of clinicians [ 77, 78], enhancing risk stratification for kidney transplant recipients [ 79], and early detecting of acute renal transplant rejection [ 80].

The studies discussed above are shown in Table 6.

2.6. Gastroenterology and Hepatology

The fields of gastroenterology and hepatology have also gathered renewed interest in using AI for improving the prediction, diagnosis, treatment, and prognosis of various conditions. For instance, Cao et al. [ 81] comparatively examined DBN, MLR, and CNN for predicting a long-term postoperative health-related quality of life after bariatric surgery. Among the tested models, DBN showed the best performance; yet, the authors considered that a hybrid network is worth investigating in the future.

Significant attention has been drawn to creating better assessment methods for celiac disease, as this condition may be associated with severe reactions (e.g., pancreatic exocrine dysfunction, microscopic colitis, and enteropathy-associated lymphoma), despite gluten-free diet adherence [ 10]. In this respect, Caetano dos Santos et al. [ 82] applied an ML algorithm that enabled a quick and precise endomysial autoantibody (EmA) test analysis for diagnosing celiac disease. In contrast, Syed and colleagues [ 83] combined a CNN with a deconvolutional network in a histopathological analysis model for identifying and differentiating between duodenal biopsies from children with environmental enteropathy and celiac disease. Alternatively, Choung et al. [ 84] aimed to discover celiac disease biomarkers derived from neoepitopes of deamidated gliadin peptides (DGP) and tTG fragments. Given the promising results obtained using a SVM, the authors concluded that this biomarker assay can be employed to detect and monitor patients with celiac disease.

Interesting recent developments have also been envisaged for procedures like endoscopy and colonoscopy. Yang et al. [ 85] used AI image recognition to obtain digestive endoscopy images, judge the disease type, and decide the treatment plan. Their 5G DL edge algorithm exhibited a high accuracy and speed, being considered promising for assisting in a medical diagnosis. On a different note, Zhou et al. [ 86] aimed to improve the bowel preparation needed for an effective colonoscopy through AI tools. The authors developed an objective and stable DCNN-based system, called ENDOANGEL, whose performance recommends it for application in clinical settings.

Numerous studies have investigated the application of AI tools for detecting, enhancing decision support, and quantifying the treatment response in patients with liver diseases. For instance, Mostafa et al. [ 87] proposed the use of ML classification methods for the prediction of liver disease in blood donors. Obtaining promising performance metrics, the tested methods (i.e., ANN, random forest, and SVM) could assist healthcare workers in distinguishing between healthy and diseased individuals. A different study conducted by Taylor-Weiner et al. [ 88] applied machine learning to characterize the disease severity and heterogeneity and quantify the treatment response in nonalcoholic steatohepatitis (NASH). The use of a DCNN led to encouraging results, paving the way for further advances in understanding the disease heterogeneity in NASH, risk stratifying the affected patients, and facilitating the development of therapies. Alternatively, Roy and colleagues [ 89] introduced a deep learning-based region-boundary integrated network for precise steatosis quantification that can enhance liver disease decision support using whole slide liver histopathology images. In another study, Gawrieh et al. [ 90] evaluated the role of AI in the detection and quantification of hepatic fibrosis in nonalcoholic fatty liver disease (NAFLD) biopsies. The automated quantification of a collagen proportionate area (CPA) was in good agreement with the pathologist score of the fibrosis stage, demonstrating reliability in accomplishing the set task.

Important works have also been reported in the subfield of liver transplantation, as the many decisions that need to be accomplished for such a procedure could benefit from the integration of AI [ 9]. Pérez-Sanz et al. [ 91] recently developed a computer vision-based application for the quantification of macrovesicular steatosis in histopathological liver section slides in pretransplant liver biopsies. The use of a Sudan stain was reflected in a reliable contrast and the facilitation of fast and accurate quantification through the tested ML algorithms. A similar approach was also proposed by Narayan et al. [ 92], who used a computer vision AI platform to score donor liver steatosis and compared its capability for predicting early allograft dysfunction. This method resulted in slightly better calibration than pathologist steatosis, paving the way for more accurate and reliable predictions of the post-transplantation outcomes.

The discussion on the clinical applications of AI in gastroenterology and hepatology is summarized in Table 7.

2.7. Orthopedics and Rheumatology

The use of AI has also reached the interconnected fields of orthopedics and rheumatology, being studied for a variety of applications. For example, Diaz-Rodriguez et al. [ 94] focused on developing a novel intraarticular (IA) injection for osteoarthritis management. The authors proposed the combination of poloxamers with hyaluronic acid in producing suitable beta-lapachone-loaded IA formulations. With the use of AI, an optimized formulation was developed based on the experimental results of a broad range of hydrogels. According to an ex vivo evaluation, the as-designed formulation exhibited excellent rheological properties and significantly decreased the secretion of degradative and proinflammatory molecules, being a promising candidate for osteoarthritis treatment.

A recent study proposed by Bayramoglu et al. [ 95] tackled the potential of analyzing patellar bone texture to predict patellofemoral osteoarthritis. Using knee lateral view radiographs, a ML model, and DCNNs, the authors obtained promising results, demonstrating that the analyzed texture features contained useful information of the patellar bone structure and could be used as additional imaging biomarkers in osteoarthritis diagnostics.

Another possible application of AI is the detection and characterization of a meniscus tear based on MRI examinations of the knee. In this respect, Roblot et al. [ 96] proposed the use of a fast region CNN for a meniscus tears diagnosis, demonstrating its accuracy in detecting the positions of the two meniscal horns, the presence of a meniscal tear, and the orientation of the tear. Similarly, Couteaux et al. [ 97] trained a mask region-based CNN with MR images to explicitly localize normal and torn menisci and classify the orientation of the tear. The model had a satisfactory performance; yet, the authors concluded that further extension of the database or the inclusion of 3D data could improve the results, especially for nontypical cases of extensively damaged menisci or multiple tears.

Rouzrokh et al. [ 98] studied the potential of a CNN model for assessing the risk of dislocation following total hip arthroplasty. Based on postoperative anteroposterior pelvis radiographs, the model could be coupled with clinical risk factor information for the rapid and accurate assessment of the risk of dislocation.

Interesting studies have also reported the use of genetic backgrounds for training AI tools, as genetic or epigenomic datasets can be employed in developing new biomarkers and finding new disease patterns and abnormalities [ 7]. In this context, Patrick et al. [ 99] utilized data from patients with genotyped psoriatic arthritis (PsA) and cutaneous-only psoriasis (PsC) to train ML algorithms for identifying differences in the genetic architecture between the two groups and assess the PsA risk before the appearance of symptoms. The authors demonstrated that the combination of statistical and ML techniques accurately identified the underlying genetic differences between the psoriasis subtypes, being of potential use in an individualized subtype risk assessment. Alternatively, another research group focused on differentiating rheumatoid arthritis (RA) from osteoarthritis (OA). Specifically, by means of ML algorithms, Long and colleagues [ 100] found a 16-gene signature, including TMOD1, POP7, SGCA, KLRD1, ALOX5, RAB22A, ANK3, PTPN3, GZMK, CLU, GZMB, FBXL7, TNFRSF4, IL32, MXRA7, and CD8A, that could effectively differentiate RA from OA. Given the model's good performance, the authors concluded that the proposed genetic signature coupled with complex classification methods holds promise for improving the diagnosis and management of RA patients.

Studies with at least one of the accuracy, precision, sensitivity, or specificity performance metrics available are summarized in Table 8.

2.8. Other Applications

Unlike AI in medicine, which uses autonomously functioning algorithms for analyzing patient data towards improving patient outcomes, AI can also be employed in surgery with the involvement of movement. Through the latest developments in DL and DCNNs, AI enables object detection and tracking, making surgical resection easier and safer [ 101, 102]. AI may also be engaged in surgical education for the assessment of surgical competencies, yet further evidence is required concerning its implementation and applicability [ 103].

The use of AI technologies also paves the way for creating patient-specific devices that can meet the exact requirements of each individual. Interest has been raised in constructing customized devices with distinct designs compared to the commercially available ones that match the anatomic particularities, physiological conditions, and pathological status of patients [ 104, 105]. Specifically, advances have been reported in developing a broad range of personalized devices, including bioprosthetic heart valves [ 106], cardiovascular stents [ 107], tissue-engineered vascular grafts [ 108], prostheses for tumor reconstruction [ 109], cranial implants [ 110, 111, 112], and dental implants [ 113].

Another potential use of AI consists of contributing to clinical trial designs and execution towards enhancing the participation and diversity within the trial populations. Specifically, the ingenious use of data in the EHR, medical literature, and trial databases can improve patient-trial matching and recruitment, subsequently increasing trial success rates [ 114, 115].

Promising perspectives also arise from the involvement of AI in the pharmaceutical industry [ 116, 117, 118, 119]. Various techniques can assess the severity of a disease and predict whether a certain treatment will be effective for an individual patient even before its administration. Moreover, AI can be employed in developing or extrapolating new applications of instruments or chemicals. Particularly, through active learning, ML-based tools can overcome concerning issues in drug design due to their ability to adapt surplus amounts of data available for generating meaningful insights [ 14] ( Figure 3). In addition, introducing AI in studies of polypharmacology, drug design, drug screening, and drug repurposing ( Figure 4) can significantly improve the efficiency and reduce the necessary time for generating treatments for various diseases, including new outbreaks like COVID-19 [ 120].

3. Challenges in AI Clinical Integration

Despite the recent advances in AI technology and the benefits it may bring to clinical care, there are many challenges that impede its translation into practice ( Figure 5). One issue is related to the legal regulation of the conditions and features of development, functioning, applicability, integration into other systems, and control over the utilization of end-to-end digital AI technology. As there is no common legal framework yet, this challenge is overcome in each country by considering the particularities of the local legal system [ 5].

In addition to the rule of law, nonlegal instruments also set important guidelines in medical activities. Hence, AI technology must also be seen from the perspective of the psychological, ethical, and moral aspects of treating patients [ 5, 121].

Another important problem faced by AI tools is skepticism, especially given by the lack of understanding of the methodology of the algorithms [ 14]. Found in the literature as the "black box" phenomenon, this challenge can be defined as the "human inability in explaining the precise steps leading to the AI tools' predictions" [ 11]. Hence, clinicians may preferentially opt for highly transparent models in which the risk factors are handled in a comprehensible way from a pathophysiological point of view. Examples of "black box" techniques are neural networks, random forests, and gradient boosting models, while, at the opposite end, "white box" algorithms can be found, such as logistic regression and decision trees. Nonetheless, there is a tradeoff between the accuracy and interpretability of these methods, causing an ongoing debate of choosing the best options for clinical applications [ 4].

Although there is a positive attitude towards engaging AI technology in clinical practice, it has been reported that there is a lack of training in students and medical doctors who are supposed to work with these innovative methods. This aspect represents an important drawback, as running AI procedures by inexperienced users may lead to biased, subjective outcomes. This problem can be solved by expanding and improving medical school training in AI through familiarizing healthcare workers and taking full advantage of these emerging technologies without disregarding ethical considerations [ 121].

Special consideration must be given to the standardization of the metrics used in AI-based studies, as researchers present their findings in quite a heterogeneous manner. In more detail, numerous performance metrics, including accuracy, sensitivity, specificity, precision, F1 score, the area under the receiver operating curve, and more, are alternatively used for demonstrating the quality of a model, yet they are difficult to compare and correlate. Additionally, the outstanding results of exclusive in silico studies may not be reflected with the same success in clinical practice [ 10, 11].

In comparison to business and industry, the medical sector also faces technological limitations in acquiring and analyzing data. As the extent of the resources required to store and analyze data can be prohibitive, it imposes additional limitations on translating AI into clinical investigations and practices. Moreover, the poor organization and management of big data in healthcare may lead to the production of inaccurate models in which erroneous data is inappropriately included [ 1, 122].

Furthermore, ML algorithms are dependent on a predefined set of data to learn from, being restricted by the information that a dataset can provide. When there is a disproportionate number of features in comparison to the amount of data in the training set, the ML model may result in overfitting, compromising the reliability of future predictions and leading to poor generalizability of the findings. Thus, for an AI system to be effectively used in medicine, sufficient data must be provided in the training stage [ 10, 123, 124]. Generally, 70% of the available dataset is allocated for training, while the remaining 30% is used for validation. Concerning the data amount, the rule is "the more data, the better"; however, a clear minimum acceptable dataset size has not been indicated. Nonetheless, learning curves (the model performance as a function of the training sample size) may provide an indication of the sample size required for effective training of the model [ 125, 126]. In addition, an algorithm applied in one environment will not automatically be suitable in another environment, requiring careful development, testing, and evaluation in each new context before implementing AI systems for patient care [ 127]. Intense debate has also arisen from the fact that AI studies tend to compare algorithm performances to clinicians when, instead, realistic applications would involve a combination of human and artificial intelligence [ 128]. This has raised concerns on whether human clinicians will become redundant with the advancements of AI technology or lose the skills they do not regularly use. Moreover, automation bias means that humans tend to agree with AI decisions, even when they are incorrect. However, as machines cannot be held responsible for their decisions, the legal liability will still be on the shoulders of physicians [ 10, 127, 128].

4. Conclusions

To summarize, artificial intelligence holds great promise for revolutionizing clinical care. By the ingenious use of big data in healthcare, ML algorithms, and neural networks, better options can be envisaged for the triage, diagnosis, prognosis, monitoring, and treatment of various challenging diseases. Numerous studies have tackled the potential use of AI in medical fields, such as cardiology, neurology, oncology, hematology, nephrology, gastroenterology, hepatology, orthopedics, and rheumatology, and in auxiliary areas, including drug design and the fabrication of patient-specific medical devices.

Nonetheless, several limitations have impeded the translation of AI developments into clinical practices. Thus, prompt solutions and clarifications are required before integrating these emerging technologies into medical protocols. Moreover, the idea that AI should be viewed as a complementary tool to the expertise of clinicians and not as a replacement alternative must be reinforced.

In conclusion, AI is a source of tremendous opportunity in clinical care, deserving considerable attention from the scientific community in order to fully understanding its benefits and develop novel tools. Through the proper exploitation of AI advantages, personalized medicine may soon become a worldwide reality.

Author Contributions

Ș.B., A.-G.N., A.B., G.E.D.P., D.N.P., I.N., M.L., M.G., O.A., A.M.G. and H.M. participated in the review, writing, and revision. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 2. Examples of AI potential applications in clinical care. Reproduced from [ 6].

Figure 3. ML in drug discovery. Reproduced from [ 14], Elsevier B.V. 2021.

Figure 4. Applications of AI in drug discovery. Adapted from [ 14].

Supervised learning

Training an ML algorithm using previously labeled training examples, consisting of inputs and desired outputs provided by an expert

Decision tree

Nonparametric supervised learning method visualized as a graph representing the choices and their outcomes in the form of a tree; each tree consists of nodes (attributes in the group to be classified) and branches (values that a node can take)

K-nearest neighbors (KNN)

"Instance-based learning" or a non-generalizing learning algorithm that does not focus on constructing a general internal model but, rather, stores all instances corresponding to the training data in an n-dimensional space and classifies new data points based on similarity measures

Artificial neural network (ANN)

An ML technique that processes information in an architecture comprising many layers ("neurons"), each inter-neuronal connection extracting the desired parameters incrementally from the training data

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# data[].attributes.attachment_link.title
Clinical Applications of Artificial Intelligence-An Updated Overview

# data[].attributes.attachment_link.description
Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care.