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New York, Jan. 19, 2021 (GLOBE NEWSWIRE) -- Adoption of advanced technologies by pharmaceutical companies and hospitals is driving the demand for artificial intelligence in healthcare market.
Market Size - USD 3.39 Billion in 2019, Market Growth - CAGR of 43.6%, Market Trends -Drug development for COVID-19.
Artificial intelligence helps medical professionals to retrieve information, interpret images, and plan therapy during medical procedures. It also eases the burden on healthcare workers. Government and private companies are focusing on incorporating artificial intelligence in research activities currently, to accelerate developments related to COVID-19 vaccines.

The growing trend of increasing use of precision medicines and increasing clinical trial processes is supporting demand for and deployment of artificial intelligence in the healthcare the sector. Increase in prevalence of chronic diseases and increasing geriatric population will contribute significantly to growth of the market. Players in the pharmaceutical market are increasingly adopting artificial intelligence to develop novel drugs and speed up overall processes. Artificial intelligence is used in research for cancer and disease management, which has been increasing in the recent past, and this is expected to drive growth of the artificial intelligence in the healthcare market over the forecast period.
The COVID-19 impact:
The market for artificial intelligence in healthcare has witnessed major increase in demand due to the coronavirus pandemic. Its usage minimizes the risk of transmission as it limits human interaction and shields frontline workers. Pharmaceutical companies are among the primary companies adopting AI for development of vaccines and to study different strains of the virus.
Further key findings from the report suggest

Growth in revenue from the hardware segment is relatively high due to increasing demand for advanced hardware from research and biopharmaceutical companies owing to need to deploy more advanced software and technologies.

Natural language processing (NLP) demand is increasing in the healthcare sector because of ability to search, analyze, and interpret vast volumes of patient data. NLP improves clinical documentation and makes computer-assisted coding more efficient, which is driving demand among healthcare professionals.

Hospitals & healthcare providers are rapidly adopting artificial intelligence due to the rising trend of telehealth in view of the COVID-19 pandemic and restrictions as a result of social distancing norms etc. AI includes the use of telediagnosis, tele-assessment, telemonitoring, and tele-interactions, for which demand been increasing substantially post the lockdown.

North America accounted for largest share in terms of revenue, as compared to other regions, in 2019 due to growing adoption of IT solutions and well-established healthcare sector. Government initiatives to adopt digital platforms in healthcare industry and sector is relatively robust in countries in the region, and is expected to continue to support growth of the market in the region.

List of Top Companies Profiled in the Artificial Intelligence (AI) in Healthcare Market:

Microsoft Corporation

Intel Corporation

IBM

Amazon Web Services

Intel Corporation

Johnson & Johnson Services, Inc.

Siemens Healthineers

General Electric

Google Inc.

For the purpose of this report, Reports and Data has segmented the global artificial intelligence in healthcare market on the basis of offering, technology, end-use, and region:

Offering Outlook (Revenue, USD Billion; 2017-2027)

Technology Outlook (Revenue, USD Billion; 2017-2027)

Machine Learning

1. Supervised Learning

2. Unsupervised Learning

3. Deep Learning

4. Reinforcement Learning

5. Others

Context-Aware Computing

Natural Language Processing

Computer Vision

End-Use Outlook (Revenue, USD Billion; 2017-2027)

Hospitals & Healthcare Providers

Pharmaceuticals & Biotechnology Companies

Patients

Healthcare Payers

Others

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Artificial Intelligence (AI) in Healthcare Market Size Worth $61.59 Billion By 2027 | CAGR of 43.6% By Reports and Data

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New York, Jan. 19, 2021 (GLOBE NEWSWIRE) -- Adoption of advanced technologies by pharmaceutical companies and hospitals is driving the demand for artificial intelligence in healthcare market. Market Size - USD 3.39 Billion in 2019, Market Growth - CAGR of 43.6%, Market Trends -Drug development for COVID-19.

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We are focused on utilizing cutting-edge technology and innovative research to develop high-value therapeutics aimed at transforming patients' lives. BioXcel Therapeutics employs a unique AI platform to reduce therapeutic development costs and potentially accelerate timelines while aiming to increase the possibility of success.

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Our mission is to make high-quality healthcare accessible and affordable for everyone on Earth.

By using AI and technology, we help our clinicians gain insights to make more informed decisions. This can help make healthcare more affordable. And to make healthcare accessible, we deliver it through the devices people already own.

We've got a world-class team from over 60 different countries. By bringing together GPs, nurses, and specialists with AI engineers and scientists, we've created a home for brilliant innovators, ambitious creators and fast builders.

Great partnerships inspire better healthcare

We want to provide scalable, sustainable services everywhere. That's why we build healthcare partnerships with organisations to solve challenges unique to different locations and populations.

In the US, we work with partners to provide 24/7 primary care and support to millions of Americans.

In the UK, we're working with the NHS to reduce wait times and ease pressure on emergency rooms.

In Rwanda, we're helping the government deliver healthcare to 20% of the total population.

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Skip to content Our mission is to make high-quality healthcare accessible and affordable for everyone on Earth. By using AI and technology, we help our clinicians gain insights to make more informed decisions. This can help make healthcare more affordable. And to make healthcare accessible, we deliver it through the devices people already own.

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https://towardsdatascience.com/how-artificial-intelligence-is-impacting-our-everyday-lives-eae3b63379e1

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How Artificial Intelligence Is Impacting Our Everyday Lives

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There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives. AI assists in every area of our lives, whether we're trying to read our emails, get driving directions, get music or movie recommendations.

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https://www.europeanbusinessreview.com/find-what-elon-musk-said-about-real-world-ai/

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The stimulation of human intellect in digital computers and controlled robotic machines that learn from experiences and are encoded to think like humans to solve multifaceted problems in an array of different frameworks and environments is famous as artificial intelligence.

It is an interdisciplinary competence with multiple approaches, but its progressions in mathematical and statistical techniques are creating an archetype shift in nearly every division of the tech industry today.

However, even when AI is remarked as one of the evolving technologies with the most incredible benefits, many tech enthusiasts and business tycoons still consider it a significant threat to humankind and calls for more investigation on its communal consequences. Some of its signatories include Elon Musk, a well-known business executive, physicist Steven Hawkens and director of research, Google, Peter Norvig.

Including Musk, many today think that digital machines will swiftly outsmart humans when real-world AI is achieved. The Tesla CEO and Product Architect, Elon Musk, has indeed made a big name in claiming that the future can get here sooner, but when the ball goes into the court of AI, Elon has very different views. He calls AI the most extensive warning to humanity.

Elon, in an interview, stated: "As AI gets probably much smarter than humans, the relative intelligence ratio is probably similar to that between a person and a cat, maybe bigger." He also mentioned, "I think we need to be very careful about the advancement of AI".

His concern, however, is no weird. With machine learning, you cannot teach a self-driving car to identify unusual conditions that come intuitively to humans, like prophesying the movement of a biker, spotting a plastic bag accompanying the breeze on a jammed road. Situations like these are complicated to teach to a robot, says Musk.

Musk thinks we are going towards a more significant mistake by deploying robust AI systems that can result in dangerous conditions for humanity or bring an end to it.

Elon musk compared the embracing of AI to" Summoning The Demon" and stated that robot leadership is more threat to the world than North Korea and could unleash "weapons of terror."

While reiterating his fears, Musk included that he believes that artificial intelligence can elicit the next world war and would come out to dominate the world. He also warns that those intelligent AI would become everlasting authoritarians from which the world could never escape.

At a National Governor's Association summer meeting, Musk communicated to the attendees and expressed that he knows, "I keeps sounding the alarm bell, but until people see robots going down the street killing people, they don't know how to react because it seems so ethereal."

One of his statements included that "we need to be super careful with AI as they are potentially more dangerous than nukes". He also recommends being proactive in regulation than reactive.

Musk has an epic saga of voicing severe concerns over the harsh potential of AI. In 2014 he tweeted that AI can be more dangerous than nukes and told his audience AI is the biggest existential threat, and humankind needs to be very vigilant about its advancement.

He stated: "with AI, we are summoning the demon. In all those stories where there's a guy with the pentagram and holy water, it's like yeah, he's sure he can control the demon. Didn't work out."

Therefore, he believes in the idea of better-regulated development of AI. To better stay safe than sorry. Even for his own brand Tesla, he wants AI to be better controlled and structured. It is acclaimed that AI organization, co-founded by Musk, has altered its mission of evolving and issuing AI securely and fairly into a secretive company fascinated with image and determined to raise more money constantly.

Corresponding to his statement of being more careful about AI's advancement, he has been drifting the goal for some government controls of AI for a bit as well.

According to an essay editing services UK, In 2018, he told Recode's Kara Swisher that we must have a government team that starts with understanding and gaining insight. According to him, the committee should spend a year or two gaining a thorough insight into AI and other technologies that can be a threat or can be dangerous. But AI specifically. After then only the team can come up with protocols to guarantee the securest use of AI.

He later added that he didn't think if such a team would actually happen.

There's now no doubt to the truth that AI is the scariest problem happening in Musk's life. He had frequently cautioned that AI would shortly become as clever as humans, and when it does, we ought to be must scare because humankind's very existence is at stake.

This tech billionaire told The New York times that his experience working with AI at Tesla means he knows what he is saying and has confidence in it. He further said that they proceed near a state where AI is more intelligent than humans, and its nature is the one that crashes humanity in every game. He also mentioned that the time frame is not more than five years, but that doesn't mean that everything will go into hell in five years; instead, things would get weird and unstable.

If I Had To Guess At What Our Biggest Existential Threat Is, It's Probably That", Says Elon Musk On AI

Musk is probably the scariest person when it comes to AI. At Massachusetts Institute of technology's 100 th anniversary, Musk had to ask the questioner to reask his question again as the question before was AI-related. He was so caught up upon it that he missed what the next questioner had asked.

Musk claims AI will outsmart humanity and overtake human civilization in less than five years. While surfing around the internet to buy coursework online UK, I came across an article that detailed how while speaking at the bipartisan national governors association in Rhode Island, Musk said that AI and robots will take over everyone's jobs and will do everything better than us.

According to him, AI is the real existential risk to humankind and people not really appreciate that.

He also inquired governors to employ preventative and proactive governmental interference because he thinks it might be too late by the time they are reactive in AI regulation. He knew he sounded alarming but said that the risks are high to allow AI to develop unfettered.

Musk Aims To Protect The World From Evil AI

Elon Musk is working hard to save the world from the AI apocalypse; therefore, he wants the technologies to be developed conscientiously and with the proper insight and oversight. He has asked governments to form teams and look over it, and if they can't, he is ready to do it himself.

Over the past few years, Elon has invested various resources into tech companies that uphold the liable development of intelligent machinery. However, he is suspected of working on technology that would give humankind a leg up in a possible AI catastrophe.

Stella Lincoln is an Assistant Editor at Master Thesis, a perfect place to reach out to if you are looking to get assignment help in the arena of AI and Machine Learning. During her leisure times, Stella loves to spend her time binge-watching tech series and documentaries.

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Find What Elon Musk Said About Real World AI - The European Business Review

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By Stella Lincoln The stimulation of human intellect in digital computers and controlled robotic machines that learn from experiences and are encoded to think like humans to solve multifaceted problems in an array of different frameworks and environments is famous as artificial intelligence. It is an interdisciplinary competence with multiple approaches, but its progressions in ...

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Simons Institute Polylogues: Algorithms and Race

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Recruiting: AI-Enabled Recruitment Platform | Pandologic

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Recruiting in healthcare already has several baseline challenges as it is, like finding and hiring top candidates from a pool that can feel limited. And despite the best intentions of attracting and bringing in the best people, no matter who they are, these recruiting efforts can be further hampered by an insidious enemy: bias.

Sometimes hiring bias is obvious (with candidates excluded because of particular personal or physical characteristics), but more often it's an unconscious bias that is keeping healthcare organizations from diversifying and broadening their recruiting. Race, gender, and family status are all blatant sources of hiring bias, but it can also be something as innocuous as seeking out candidates from a particular school, or unconsciously leaning toward particular types of names on a resume. You (and your organization) may not even notice that you're tipping the scales in favor of a specific group, but it still happens sometimes, due to human nature and personal biases that we may not even realize we have.

This is where technology can help. Because AI is about as impartial as it gets, it can be used to save us from ourselves. For healthcare organizations, that means using AI to refine recruiting and hiring processes so that you're setting the higher-level objectives while taking out some of the early, lower-level personal decisions that can be rife with bias.

Using AI for more diverse healthcare recruiting

Because the healthcare industry is growing so rapidly, candidates are likely to come from all corners. But are your processes ready to take advantage of that wide range of talent sources? Because AI platforms are based solely on data, and not personal discernment, you can use it to cast a wide net in the healthcare talent pool.

AI platforms will screen candidates based on the criteria you set. If you're concerned about bias entering into the process, you can have it exclude factors that are known to lead to hiring bias danger zones.

For example, studies have repeatedly shown that candidates with strongly minority-identified names still receive between 30 and 50 percent fewer callbacks and interview offers than their "white"-sounding counterparts. That's a huge problem, especially when the healthcare industry is attracting people from all backgrounds and walks of life. Using AI to screen applicants would allow you to exclude things like name, location, and school names and focus on more substantive qualifications and keywords instead.

AI helps ensure EEOC compliance

Some AI platforms are able to take candidate and employee data and check them against set metrics, like EEOC guidelines and percentages. If you're looking for a blind, more compliant hiring process for your healthcare organization, all of this can be done on the back end, without any concern that your (human) reviewers are subjecting candidates to their own personal biases.

AI improves in-person hiring practices

The interview is potentially the most bias-affected part of the recruiting and hiring process. After all, it's likely when you meet and speak with a candidate for the first time, kicking off all sorts of possible unconscious biases. AI can't yet replace the need for face-to-face interviews, but you can use it to supplement your process and cut down on the opportunities for bias. Some AI programs can generate a setlist of interview questions, ensuring that you're giving each candidate the same shake. With data-driven questions rather than personally-driven ones, recruiters and hiring managers are less likely to stray into hiring bias danger zones.

Using AI software for interviews (in addition to initial screening) isn't meant to limit your discretion and decision-making when it comes to hiring. Rather, it's used to create a standardized environment for all candidates, helping you to find the best talent for your organization. Candidates are measured against metrics, not against personal opinions, giving you a more data-fied approach to hiring.

As the healthcare industry grows and the talent pool grows more selective, it's important to make sure that you're sourcing and bringing in the best possible candidates. Moving to a data-based, AI-driven approach can help you manage even the least visible biases and ensure that you're not dismissing great candidates before they even get through your door.

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How AI Eliminates Hiring Bias in Healthcare Recruiting

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Recruiting in healthcare already has several baseline challenges as it is, like finding and hiring top candidates from a pool that can feel limited. And despite the best intentions of attracting and bringing in the best people, no matter who they are, these recruiting efforts can be further hampered by an insidious enemy: bias.

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Artificial intelligence (AI) is reshaping healthcare, and its use is becoming a reality in many medical fields and specialties. AI, machine learning (ML), natural language processing (NLP) and deep learning (DL) enable healthcare stakeholders and medical professionals to identify healthcare needs and solutions faster with more accuracy, using data patterns to make informed medical or business decisions quickly.
How AI works in healthcare
AI is able to analyze large amounts of data stored by healthcare organizations in the form of images, clinical research trials and medical claims, and can identify patterns and insights often undetectable by manual human skill sets.

AI algorithms are "taught" to identify and label data patterns, while NLP allows these algorithms to isolate relevant data. With DL, the data is analyzed and interpreted with the help of extended knowledge by computers. The impact of these tools is huge, considering a Frost & Sullivan analysis indicated artificial intelligence and cognitive computing systems in healthcare will account for $6.7 billion this year from the market compared to $811 million in 2015.

The use of AI is supporting many stakeholders in healthcare:

Teams of clinicians, researchers or data managers involved in clinical trials can speed up the process of medical coding search and confirmation, crucial in conducting and concluding clinical studies.

Healthcare payers can personalize their health plans by connecting a virtual agent via conversational AI with members interested in customized health solutions.

Clinicians can improve and customize care to patients by combing through medical data to predict or diagnose disease faster.

Top 10 uses of AI in healthcare 1. AI supports medical imaging analysis
AI is used as a tool for case triage. It supports a clinician reviewing images and scans. This enables radiologists or cardiologists to identify essential insights for prioritizing critical cases, to avoid potential errors in reading electronic health records (EHRs) and to establish more precise diagnoses.

A clinical study can result in huge amounts of data and images that need to be checked. AI algorithms can analyze these datasets at high speed and compare them to other studies in order to identify patterns and out-of-sight interconnections. The process enables medical imaging professionals to track crucial information quickly.

For example, Hardin Memorial Health (HMH) needed to find a way to extract relevant data from EHRs in a concentrated form for imaging professionals. The hospital's Emergency Room (ER) was handling more than 70,000 patients per year and decided to partner with IBMto implement "The Patient Synopsis". This product identifies patient information relevant to the imaging procedure conducted on that patient.

Patient Synopsis digs into past diagnostics and medical procedures, lab results, medical history and existing allergies, and delivers to radiologists and cardiologists a summary that focuses on the context for these images. The product can be integrated with any medical unit system structure, accessed from any communication workstation or device in the network, and upgraded without affecting the daily activity of the medical unit.

Detecting relevant issues and presenting them to radiologists in a friendly summary view enables the design of more customized, targeted and accurate report used in diagnostic decision process.
2. AI can decrease the cost to develop medicines
Supercomputers have been used to predict from databases of molecular structures which potential medicines would and would not be effective for various diseases. By using convolutional neural networks, a technology similar to the one that makes cars drive by themselves, AtomNet was able to predict the binding of small molecules to proteins by analyzing hints from millions of experimental measurements and thousands of protein structures.

This process enabled convolutional neural networks to identify a safe and effective drug candidate from the database searched, reducing the cost of developing medicine.
3. AI analyzes unstructured data
In 2015, during the West African Ebola virus outbreak, Atomwise partnered with IBM and the University of Toronto to screen the top compounds capable of binding to a glycoprotein that prevented Ebola virus penetration into cells in an in vivo (in the living body of an animal or plant) test. From the tested compounds, the one selected was chosen because it acted on other viruses with a similar mechanism of cell penetration. This AI analysis occurred in less than a day, a process that would have usually taken months or years, enabling the development of a treatment for the Ebola virus.

Clinicians often struggle to stay updated with the latest medical advances while providing quality patient-centered care due to huge amounts of health data and medical records. EHRs and biomedical data curated by medical units and medical professionals can be quickly scanned by ML technologies to provide prompt, reliable answers to clinicians.
4. AI builds complex and consolidated platforms for drug discovery
In many cases, health data and medical records of patients are stored as complicated unstructured data, which makes it difficult to interpret and access. AI can seek, collect, store and standardize medical data regardless of the format, assisting repetitive tasks and supporting clinicians with fast, accurate, tailored treatment plans and medicine for their patients instead of being buried under the weight of searching, identifying, collecting and transcribing the solutions they need from piles of paper formatted EHRs.

AI algorithms are able to identify new drug applications, tracing their toxic potential as well as their mechanisms of action. This technology led to the foundation of a drug discovery platform that enables the company to repurpose existing drugs and bioactive compounds.

By combining the best elements of biology, data science and chemistry with automation and the latest AI advances, the founding company of this platform is able to generate around 80 terabytes of biological data that is processed by AI tools across 1.5 million experiments weekly.
5. AI can forecast kidney disease
The ML tools are created to draw insights from biological datasets that are too complex for human interpretation, decreasing the risk for human bias. Identifying new uses for known drugs is an appealing strategy for Big Pharma companies, since it is less expensive to repurpose and reposition existing drugs than to create them from scratch.

Acute kidney injury (AKI) can be difficult to detect by clinicians, but can cause patients to deteriorate very fast and become life-threatening. With an estimated 11% of deaths in hospitals following a failure to identify and treat patients, the early prediction and treatment of these cases can have a huge impact to reduce life-long treatment and the cost of kidney dialysis.

In 2019, the Department of Veterans Affairs (VA) and DeepMind Health created a ML tool that can predict AKI up to 48 hours in advance. The AI tool was able to identify more than 90% of acute AKI cases 48 hours earlier than with traditional care methods.
6. AI provides valuable assistance to emergency medical staff
The partnership between VA and DeepMind Health continues. Its next target is to identify how this ML tool can be installed in medical units. A user-friendly platform is also targeted in order to support clinicians in their treatment decisions that would improve the quality of life for Veterans suffering from AKI.

During a sudden heart attack, the time between the 911 call to the ambulance arrival is crucial for recovery. For an increased chance of survival, emergency dispatchers must be able to recognize the symptoms of a cardiac arrest in order to take appropriate measures. AI can analyze both verbal and nonverbal clues in order to establish a diagnostic from a distance.

Corti is an AI tool that assists emergency medicine staff. By analyzing the voice of the caller, background noise and relevant data from medical history of the patient, Corti alerts emergency staff if it detects a heart attack. Like other ML technologies, Corti does not search for particular signals, but it trains itself by listening to many calls in order to detect crucial factors.

Based on this learning, Corti improves its model as an ongoing process. The technology Corti is equipped with can detect the difference between background noise, such as sirens, and clues from the caller, or the patient sounds in the background.

In Copenhagen, emergency dispatchers are able to identify a cardiac arrest based on the description provided by the caller around 73% of the time. But AI can do better. A small-scale study conducted in 2019 revealed that ML models were able to recognized cardiac arrest calls better than human dispatchers by using speech recognition software, ML and other background clues.
7. AI contributes to cancer research and treatment, especially in radiation therapy
ML can play an essential role in supporting emergency medical staff. In the future medical units could use the technology to respond to emergency calls with automatic defibrillators equipped drones or with CPR-trained volunteers, which would increase the chances for survival in cases of cardiac arrest that take place in the community.

In some cases, radiation therapy can lack a digital database to collect and organize EHRs, which makes the research and treatment of cancer difficult. In order to assist clinicians to make informed decisions regarding radiation therapy for cancer patients, Oncora Medical delivered a platform that collects the relevant medical data of patients, evaluates the quality of care provided, optimizes treatments and provides thorough oncology outcomes, data and imaging.
8. AI uses data collected for predictive analytics
Automatic generation of clinical notes integrated with EHRs led to a reduction of time spent by clinicians in managing patient documentation, which improves medical operations and health outcomes.

Turning EHRs into an AI-driven predictive tool allows clinicians to be more effective with their workflows, medical decisions and treatment plan. NLP and ML can read the entire medical history of a patient in real time, connect it with symptoms, chronic affections or an illness that affects other members of the family. They can turn the result into a predictive analytics tool that can catch and treat a disease before it becomes life-threatening.

In essence, chronic diseases can be predicted and their progression rate tracked. CloudMedX is a company that focuses on decoding unstructured data - data stored as notes (clinician notes, discharge summaries, diagnosis and hospitalization notes, etc.).
9. AI accelerates the discovery and development of genetic medicine
These notes are used alongside EHRs as a source to generate clinical insights for medical professionals, allowing for data-driven decisions to improve patient outcomes. CloudMedX solutions have already been applied in several high-risk diseases such as renal failure, pneumonia, congestive heart failure, hypertension, liver cancer, diabetes, orthopedic surgery and stroke, with the stated objective to lower costs for patients and clinicians by assisting in early and accurate diagnoses of patients.

AI is also used to help rapidly discover and develop medicine, with a high rate of success. Genetic diseases are favored by altered molecular phenotypes, such as protein binding. Predicting these alterations means predicting the likelihood of genetic diseases emerging. This is possible by collecting data on all identified compounds and on biomarkers relevant to certain clinical trials.

This data is processed, for example, by the AI system of Deep Genomics. The company designs proprietary AI and uses it to discover new methods to fix the consequences of genetic mutations, while developing customized therapies for people suffering from rare Mendelian and complex disease.

The company tests identified compounds in order to develop faster genetic medicine for conditions with high unmet need. The company's experts are working on " Project Saturn," a drug system based on AI molecular biology that assesses more than 69 billion oligonucleotide molecules in silico (conducted or produced by means of computer modeling or computer simulation) against 1 million target sites in order to monitor cell biology to unlock greater potential treatments and therapies.
10. AI supports health equity
The discovery and development of genetic medicine brings benefits to patients and clinicians by decreasing the costs associated with the treatment of rare diseases.

The AI and ML industry has the responsibility to design healthcare systems and tools that ensure fairness and equality are met, both in data science and in clinical studies, in order to deliver the best possible health outcomes. With more use of ML algorithms in various areas of medicine, the risk of health inequities can occur.

Those responsible for applying AI in healthcare must ensure AI algorithms are not only accurate, but objective and fair. Since many clinical trial guidelines and diagnostic tests take into account a patient's race and ethnicity that a debate has arisen:

Is the selection of these factors evidence-based? Is race and ethnicity data more likely to solve or to increase universal health inequities? It is established that ML comprises a set of methods that enables computers to learn from the data they process. That means that, at least in principle, ML can provide unbiased predictions based only on the impartial analysis of the underlying data.
CONCLUSION
AI and ML algorithms can be educated to decrease or remove bias by promoting data transparency and diversity for reducing health inequities. Healthcare research in AI and ML has the potential to eliminate health-outcome differences based on race, ethnicity or gender.

AI adoption in healthcare continues to have challenges, such as lack of trust in the results delivered by an ML system and the need to meet specific requirements. However, the use of AI in health has already brought multiple benefits to healthcare stakeholders.
About the Author
By improving workflows and operations, assisting medical and nonmedical staff with repetitive tasks, supporting users in finding faster answers to inquiries, and developing innovative treatments and therapies, patients, payers, researchers and clinicians can all benefit from the use of AI in healthcare.

Dr. Liz Kwo a serial healthcare entrepreneur, physician and Harvard Medical School faculty lecturer. She received an MD from Harvard Medical School, an MBA from Harvard Business School and an MPH from the Harvard T.H. Chan School of Public Health.

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Contributed: Top 10 Use Cases for AI in Healthcare

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Artificial intelligence (AI) is reshaping healthcare, and its use is becoming a reality in many medical fields and specialties. AI, machine learning (ML), natural language processing (NLP) and deep learning (DL) enable healthcare stakeholders and medical professionals to identify healthcare needs and solutions faster with more accuracy, using data patterns to make informed medical or business decisions quickly.

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- What is AI in healthcare ✅

- How the healthcare system has benefited from AI✅
- How AI bias leads to improper care and diagnoses ✅
- Case study: Race and ethnicity bias ✅
- Ways to reduce AI bias in the healthcare system✅

- Future plans for AI in healthcare

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New research examines healthcare data and machine learning models routinely used in both research and application to address bias in healthcare AI.

Artificial intelligence keeps inching its way into more and more aspects of our life, greatly benefitting the world. But it can come with some strings attached, such as bias.

AI algorithms can both reflect and propagate bias, causing unintended harm. Especially in a field such as in healthcare.

Real-world data give us a way to understand how AI bias emerges, how to address it and what's at stake. That's what we have done in our recent study, focused on a clinical scenario, where AI systems are built on observational data taken during routine medical care. Often such data reflects underlying societal inequalities, in ways that are not always obvious - and this AI bias could have devastating results on patients' wellbeing.

Our team of researchers, from IBM Research and Watson Health, have diverse backgrounds in medicine, computer science, machine learning, epidemiology, statistics, informatics and health equity research. The study - " Comparison of methods to reduce bias from clinical prediction models of postpartum depression," recently published in JAMA Network Open - takes advantage of this interdisciplinary lineup to examine healthcare data and machine learning models routinely used in research and application.

We analyzed postpartum depression (PPD) and mental health service use among a group of women who use Medicaid, a health coverage provider to many Americans. We evaluated the data and models for the presence of algorithmic bias, aiming to introduce and assess methods that could help reduce it, and found that bias could create serious disadvantages for racial and ethnic minorities.

We believe that our approach to detect, assess and reduce bias could be applied to many clinical prediction models before deployment, to help clinical researchers and practitioners use machine learning methods fairer and more effectively.

What is AI fairness, anyway?

Over the past decades, there has been a lot of work addressing AI bias. One landmark study recently showed how an algorithm, which was built to predict which patients with complex health needs would cost the health system more, disadvantaged Black patients due to unrecognized racial bias in interpreting the data. The algorithm used healthcare costs incurred by a patient as a proxy label to predict medical needs and provide additional care resources. While this might seem logical, it does not account for the fact that Black patients had lower costs at the same level of need as white patients in the data and were therefore missed by the algorithm.

To deal with the bias - to 'debias' an algorithm - researchers typically measure the level of fairness in AI predictions. Fairness is often defined with respect to the relationship between a sensitive attribute, such as a demographic characteristic like race or gender, and an outcome. Debiasing methods try to reduce or eliminate differences across groups or individuals defined by a sensitive attribute. IBM is leading this effort by creating AI Fairness 360, an open-source python toolkit that allows researchers to apply existing debiasing methods in their work.

But applying these techniques is not trivial.

There is no consensus on how to measure fairness, or even on what fairness means, which is shown by conflicting and incompatible metrics of fairness. For example, should fairness be measured by comparing what the model predicted or the accuracy of the models? Also, in most cases it is not clearly known how and why outcomes differ by sensitive attributes like race. As a result, a great deal of prior work has been done using simulated data or by using simplified examples that do not reflect the complexity of real-world scenarios in healthcare.

There is no consensus on how to measure fairness, or even on what fairness means, which is shown by conflicting and incompatible metrics of fairness.

So we decided to use a real-world scenario instead. As researchers in healthcare and AI, we wanted to demonstrate how recent advances in fairness-aware machine learning approaches can be applied to clinical use cases so that people can learn and use those methods in practice.

Debiasing with Prejudice Remover and reweighing

PPD affects one in nine women in the US who give birth, and early detection has significant implications for maternal and child health. Incidence is higher among women with low socioeconomic status, such as Medicaid enrollees. Despite prior evidence indicating similar PPD rates across racial and ethnic groups, under-diagnosis and under-treatment has been observed among minorities on Medicaid. Varying rates of reported PPD reflect the complex dynamics of perceived stigma, cultural differences, patient-provider relationships and clinical needs in minority populations.

We focused on predicting postpartum depression and postpartum mental health care use among pregnant women in Medicaid. We used the IBM MarketScan Research Database, which contains a rich set of patient-level features for the study.

Our approach had two components. First, we assessed whether there was evidence of bias in the training data used to create the model. After accounting for demographic and clinical differences, we observed that white females were twice as likely as Black females to be diagnosed with PPD and were also more likely to use mental health services, post-partum.

This result is in contrast to what is reported in medical literature - that the incidence of postpartum depressive symptoms is comparable or even higher among minority women - and possibly points to disparity in access, diagnosis and treatment.

It means that unless there is a documented reason to believe that white females with similar clinical characteristics to Black females in this study population would be more susceptible to developing PPD, the observed difference in outcome is likely due to bias arising from underlying inequity. In other words, machine learning models built with this data for resource allocation will favor white women over Black women.

We then successfully reduced this bias by applying debiasing methods called reweighing and Prejudice Remover to our models through the AI Fairness 360 Toolkit . These methods mitigate bias by reducing the effect of race in prediction through weighting training data or modifying algorithm's objective function.

We compared the two methods to the so-called Fairness Through Unawareness (FTU) method that simply removes race from the model. We quantified fairness using two different methods to overcome the limitations of imperfect metrics. We showed that the two debiasing methods resulted in models that would allocate more resources to Black females compared to the baseline or the FTU model.

As we've shown, clinical prediction models trained on potentially biased data could produce unfair outcomes for patients. In conducting our research we used the types of ML models increasingly applied to healthcare use cases, so our results should get both researchers and clinicians think about bias issues and ways to mitigate any possible bias before implementing AI algorithms in care.

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IBM researchers investigate ways to help reduce bias in healthcare AI | IBM Research Blog

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New research examines healthcare data and machine learning models routinely used in both research and application to address bias in healthcare AI.

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Artificial Intelligence Bias in Healthcare

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Understanding the impact human bias has on AI data and algorithms in healthcare delivery systems.

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AI algorithms in healthcare often tend to adopt unwanted biases inadvertently, leading to improper diagnosis and care recommendations. Explainable AI could be key to resolving it.
The advent of artificial intelligence (AI) has resulted in the use of aggregated data in healthcare to develop complex models to automate diagnosis. This has empowered a practitioner's approach by tailoring treatments and focusing on patients effectively. AI in Healthcare is expected to reach $36.15 billion by 2025 with a growth rate of 50.2%.[i] Hospitals are being built on the premise that AI systems are the future, and start-ups using AI for healthcare have raised more than $5 billion in venture capital[ii].

Most improvements in AI systems are made in view of advances in the field of Machine Learning and Deep Learning. In contrast to traditional systems, where rules are manually crafted, AI systems are developed based on past data and examples. For instance, engineers created an algorithm to help diagnose breast cancer and fed it to mammograms to identify cancer cells. The algorithms processed the models and discovered a common pattern matching over 60,000 patients to distinguish cancerous cells from non-cancerous ones with an accuracy of 31% compared to 18% without it, and cancer risk could be predicted up to 5 years in advance[iii]. AI systems have also performed better than humans in areas such as radiology and medical imaging.

However, one of the fundamental problems of Learning Algorithms is that they tend to adopt unwanted biases based on data utilized for training. In Healthcare, this can lead to improper diagnosis and care recommendations. Recently, several stories involving AI algorithmic bias have emerged, where there is a tendency of AI to favor certain groups based on gender, age, and race. AI algorithmic bias in the field of medicine could prove to be fatal[iv].
Biased data sets for AI algorithms:
Data plays a major role as far as biases creeping into the process is concerned. For instance, a Canadian company developed an algorithm for auditory tests for neurological diseases[i]. It registers the way a person speaks and analyzes data to determine early stage Alzheimer's disease. The test had >90% accuracy; however, the data consisted of samples of native English speakers only. Hence, when non-English speakers took the test, it would identify pauses and mispronunciations as indicators of the disease.

In a similar vein, an algorithm was developed to diagnose maligning skin lesions from images[ii]. People with white skin are more likely to suffer from skin cancer, hence, more data was available for that particular skin type. The system was 11%-19% more accurate in diagnosing people with light skin, but was 34% less accurate on darker skinned individuals. The algorithm was trained on a patient dataset containing a very small sample size of a certain patient type and is not suitable for clinical use in a hospital with a patient population comprising a wide variety of skin types.

The above examples highlight biases occurring due to misrepresentation of patient population data while training an algorithm. Hence, while designing, it is paramount to consider all known factors related to the patient population.
Lack of Transparency
While most algorithmic bias studies are focused on determining known factors i.e. gender, race, age etc., recent research shows that algorithms can gather hidden biases that are often difficult to identify. Therefore, it is often arduous and developers have very little visibility to identify and fix issues.

In some cases, biases tend to occur due to a correlation between the factors considered such as the training data for an algorithm designed to determine the hospital stay/admission of a person. The model assigned low risk scores to asthmatic patients and high scores to patients with pneumonia. It would recommend admission to less critical pneumonia patients rather than high critical asthma cases[iii]. Hence, output strongly depended on certain factors while failing to consider critical factors.

By either failing to consider the right subjects for datasets or wrong factors for training, there are many situations where crucial decisions can be influenced by bias. It is evident that bias is a challenge, but not impossible to overcome.

In order to address biases, the first step is to understand and accept the fact that data could be a problem[i]. Second, determine how and why algorithms are biased to take certain decisions. An explainable and interpretable AI would allow developers, business users, and end users to fathom why certain decisions are made. Subsequently, the necessary course correction can be initiated. Machine Learning models are often black boxes that offer very little visibility into their inner workings. This is where explainability comes in. It provides justification for the results in an interpretable way.

An Explainable AI will help to build trust in an AI algorithm, which is critical in the healthcare system. This ensures fairness, inclusivity, reliability, and transparency while designing and maintaining privacy and driving accountability. This involves monitoring data for inequalities, as well as talking to healthcare providers and patients to determine if they see any fairness conundrums. This could enable organizations to not only identify potential discrimination, but also rectify algorithms beforehand[ii].

Current AI models are limited to offering predictions but as they improve, models could evolve toward explainability paired with medical reasoning and AI outcomes, which would lead to fair decision-making to increase adoption and enable trust of AI systems in healthcare.

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The Pitfalls of AI bias in Healthcare - Wipro

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AI algorithms in healthcare often tend to adopt unwanted biases inadvertently, leading to improper diagnosis and care recommendations. Explainable AI could be key to resolving it. The advent of artificial intelligence (AI) has resulted in the use of aggregated data in healthcare to develop complex models to automate diagnosis.