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Healthcare is vital for everybody and is a part of everyday life because the field plays many important roles, such as preventing diseases and injuries; diagnosing health conditions; treating illnesses and injuries; rehabilitation, which aims to help patients regain their function and independence after an illness or injury; and long-term care, which provides ongoing support and care for individuals with chronic conditions or disabilities. Healthcare is moving more towards AI every day. AI is transforming healthcare by improving diagnostic accuracy, specifically by reducing diagnostic errors and enhancing precision in various medical procedures. It also personalizes treatments for patients, automates administrative tasks, and enhances patient care through tools such as predictive analysis and virtual health assistants. AI also analyzes vast amounts of medical data to identify different patterns and risk factors; assists in drug discovery by accelerating the identification of drug targets; optimizes the design of new medicines; assists in clinical trials; and enables more efficient healthcare operations. While considering the advancement of AI in healthcare, it's also important to consider the challenges and ethical concerns that AI in healthcare carries. For example, there are issues concerning patient privacy, data bias, and consent, which need careful management. Clear legal frameworks and robust governance are essential for the safe and ethical implementation of AI in healthcare. Representative datasets are recommended to avoid biased outcomes due to the fact that AI models are only as good as the data they are trained on by a human. These challenges and ethical issues must be properly addressed and handled to ensure responsible, ethical, and effective integration of AI in healthcare. I chose this industry because healthcare is truly vital for every human being on earth and affects millions of people every day. So many people rely heavily on healthcare, and this industry also saves lives through the prevention of diseases and the treatment of illnesses. My interest in healthcare stems from having two family members who are nurses. As AI advances, they will encounter more of what AI does in healthcare. I'm really interested to learn how AI may affect them. In this Padlet wall, I will cover examples of AI usage in healthcare, future trends and ethical considerations of AI in the field, the societal impact of AI in healthcare, an overview video of my Padlet, and a reflection. Healthcare and AI applications within it are important to me because I want to be a healthcare worker, specifically a Licensed Clinical Social Worker. I want to develop a solid understanding of how this industry is affected by AI and how to navigate it, knowing that AI will only advance as time goes on, especially by the time I enter the industry.

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AI is being used in healthcare in various ways. AI improves patient care, enhances diagnostics, and streamlines operations. One use of AI in healthcare is for diagnosis and prognosis. AI detects diseases such as cancer or pneumonia by analyzing medical images, including X-rays, MRIs, and CT scans. "AI systems have been used to analyze medical images, such as those collected via X-rays or MRI scans. These tools help in diagnosing conditions like cancer, retinal diseases, pneumonia, and more." (Examples of AI used in health care - st. george's university 2025). AI can also predict an individual's risk of heart attacks and other serious health events based on that patient's data. AI is also used to personalize treatments for each patient based on their medical history, genetics, and lifestyle; provide real-time insights to assist healthcare professionals in making informed treatment decisions; analyze patient data from wearable devices and other sensors to track their health and proactively intervene; monitor medication usage and alert patients about potential problems; and perform administrative tasks such as medical coding and patient scheduling. "For instance, an AI-driven system might analyze the clinician’s schedule and automatically suggest optimal appointment times based on patient needs and availability, thereby improving the overall efficiency of the practice." (Marlene M. Maheu, "Ai use cases in healthcare" : A strategic AI & chatgpt guide 2025). AI also plays a role in accelerating drug discovery by analyzing vast amounts of data to identify potential drug candidates, predict their effects, and optimize clinical trials. The technology that's used in drug discovery includes machine learning models, which predict molecular properties and identify promising compounds with a high likelihood of binding to specific targets; and deep learning as well as neural networks, which are used to analyze molecular structures and predict how a compound will interact with biological systems. "AI techniques, particularly machine learning and deep learning, have revolutionized drug discovery by analyzing large datasets, predicting molecular properties, and identifying potential drug candidates." (Kokudeva et al., Artificial Intelligence as a tool in drug discovery and development 2024). Predictive modeling is also used, in which AI forecasts potential outcomes and optimizes the design of clinical trials by helping to select eligible patients and reduce the amount of inconsistencies in testing protocols; and finally, natural language processing, which is used to analyze scientific literature and patient data to identify new drug targets and support the research process. AI is also used in robotic surgery to enhance precision, control, and efficiency, primarily through enhanced visual guidance and automated tasks. "AI applications in robotic surgery focused on automating tasks like suturing and tissue dissection to enhance consistency and reduce surgeon workload. Present AI-driven systems incorporate functionalities such as image recognition, motion control, and haptic feedback, allowing real-time analysis of surgical field images and optimizing instrument movements for surgeons. The advantages of AI integration include enhanced precision, reduced surgeon fatigue, and improved safety." (Iftikhar et al., Artificial Intelligence: Revolutionizing robotic surgery: Review 2024). Technologies used include computer vision, in which surgical video feeds are analyzed by AI in real-time to identify critical anatomical structures and provide surgeons with enhanced visualization. Computer vision can also enhance images by correcting issues like smoke from electrocautery. Other techniques include machine learning and deep learning, in which algorithms analyze surgical movements and outcomes to automate repetitive tasks, such as suturing, and provide real-time guidance. Haptic feedback is used by some advanced systems that include AI and sensors to provide tactile feedback to the surgeon, translating tissue stiffness and resistance, which is often lost in traditional robotic surgery. Finally, reinforcement learning is used, which allows AI systems to learn through trial and error, paving the way for more autonomous surgical functions in the future. AI is also used in clinical documentation, which alleviates the administrative burden on clinicians, helping to combat burnout and increase face-to-face time with patients. The technologies used are automated speech recognition, which converts spoken language into text with high accuracy, even for complex medical terminology. "Ambient AI voice recognition systems can turn conversations between clinicians and patients into structured medical notes." (IMOSolutions, The future of clinical documentation is ambient, automated, and AI-powered 2025). Natural language processing is also used, which allows computers to understand, interpret, and generate human language. It turns unstructured data, such as dictated notes, into structured data for the electronic health record. Generative AI (Large Language Models) is also used and trained on massive text datasets and can create text that is high-quality and human-like.

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Insilico Medicine utilizes AI to design and test new drug molecules, thereby shortening the drug discovery process and reducing costs. The technology helps identify promising new compounds and assess their efficacy and safety earlier in the process. Machine learning algorithms are utilized to analyze large chemical and genomic databases to predict how molecules will bind to disease targets. Improvements and benefits brought by AI in drug discovery include faster discovery of drugs; being able to identify previously unknown or overlooked biological targets for new drugs, which expands the range of potential treatments; improved clinical trial design and outcomes; cost and time reduction; efficient resource use; and enhanced drug efficacy and precision. Challenges and limitations faced in drug discovery due to AI include data issues, including data biases, and lack of negative data; AI's "black box" nature; developing models to validate AI models and address inherent biases; job displacement; ethical considerations surrounding sensitive patient data; biological complexity that AI models may struggle to fully account for; resource requirements; and the need for traditional integration to ensure the safety and efficacy of drugs. I find AI in drug discovery interesting due to its potential for faster and more efficient treatments, more effective personalized medicines, which can reduce the risk of adverse effects and increase a drug's effectiveness, as well as offering new hope for individuals suffering from hard-to-treat diseases. Potential impacts include reduced costs, increased success rates, a significant increase in the number of new and effective therapies for a wide range of diseases, and faster timelines when it comes to developing new drugs and getting new therapies to patients. I think that AI is going to do amazing things for drug discovery. I believe that this is one of many good, effective ways to use AI, and many people will benefit from it.

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How AI is accelerating drug discovery - Nature's Building Blocks | BBC StoryWorks

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Healthcare - AI in Medical Imaging and Diagnostics

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Healthcare - AI in Medical Imaging and Diagnostics

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Aidoc's AI platform analyzes medical images in real-time to automatically flag critical findings, such as brain bleeds or strokes. This allows radiologists to prioritize urgent cases and speed up the interpretation process. The technologies used in this area include deep learning and computer vision models that are trained on vast datasets of X-rays, CT scans, and MRIs to detect subtle anomalies. Benefits and improvements brought by AI in medical imaging and diagnostics include improved diagnostic accuracy, increased efficiency, enhanced personalized medicine for patients, better workflow and resource management, early detection and prevention, and improved image quality. Challenges and limitations faced in medical imaging and diagnostics due to AI include data-related issues such as a lack of high-quality datasets, bias in data, and non-representative data; lack of transparency; system integration; hardware and computational limits; suboptimal performance; data security and privacy concerns; accountability issues; ethical considerations; lack of clinical validation; need for clinical training; cost of implementation; and resistance of healthcare professionals to change. I'm interested in AI in medical imaging and diagnostics due to AI's ability to save patients' lives by detecting life-threatening conditions and preventing diseases. Potential impacts of AI on this area include improved patient outcomes through faster analysis and personalized treatment plans; enhanced diagnostic accuracy and early detection; and accelerated imaging procedures. I think that using AI in this aspect is super beneficial and will save many lives while also reducing burdens on healthcare workers.

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Medical Imaging: From X rays to AI Assisted Diagnosis

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Medical Imaging: From X rays to AI Assisted Diagnosis

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Medical Imaging: From X rays to AI Assisted Diagnosis

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Healthcare - AI in Predictive Patient Monitoring

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Healthcare - AI in Predictive Patient Monitoring

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Johns Hopkins University developed an AI model that analyzes real-time patient data to predict the risk of sepsis in ICU patients. The technology used is algorithms that analyze real-time data from electronic health records, vital sign monitors, and wearable sensors. Benefits brought by AI in predictive patient monitoring include improved health outcomes, early disease detection, personalized treatment plans, enhanced patient safety, proactive and preventative care, increased accessibility, operational efficiency, cost savings, and better resource management. Improvements brought by AI in predictive patient monitoring include data-driven decision-making that helps healthcare professionals make more informed decisions regarding diagnosis, treatment, and care planning; precision medicine; support for nurses and clinicians by acting as a clinical decision support system; and the enhancement of remote patient monitoring by analyzing data from wearables and health apps in real-time, providing continuous feedback and early identification of potential problems for chronic disease management. Challenges and limitations faced by predictive patient monitoring brought by AI include issues with data privacy and security; algorithmic bias, which leads to unequal treatment; lack of transparency and explainability; potential for overreliance on AI and loss of clinical judgment; challenges in accountability; high implementation costs; and resistance to adoption of AI due to lack of trust. I'm interested in this because AI is enabling proactive, personalized, and cost-effective healthcare, which significantly improves outcomes for patients and increases efficiency for healthcare providers. AI is doing this by collecting and analyzing real-time data from wearables, medical devices, and electronic health records to forecast potential health issues before they can become critical. Potential impacts include earlier and more accurate disease detection, personalized treatment plans, and remote monitoring via wearables and apps, which enable proactive interventions, reduce hospitalizations and costs, and prevent complications. Another potential impact is improved overall patient outcomes and quality of life. I believe that AI being used in predictive patient monitoring is a very good idea and will benefit many patients as well as decrease healthcare workers' stress while reducing costs. I also believe that AI in this area will save lives by being proactive and preventative.

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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Future AI trends in healthcare include early disease detection via patient monitoring and predictive analytics; personalized medicine via tailored treatment plans and drug discovery; enhanced diagnostics via advanced medical imaging analysis; robotic assistance in surgery and patient care; streamlined operations via the automation of administrative tasks, such as billing, patient documentation, and data management, which reduces the administrative burden on healthcare professionals, alleviates burnout, and frees up staff to focus more on patients; workflow improvement via AI’s ability to analyze patient flow, resource utilization, and staffing patterns; streamlined hospital management which can be done by AI optimizing resource allocation and predict patient flow to improve hospital operations and efficiency; clinical trial optimization via AI’s ability to improve the process of matching patients to the most promising clinical trials, which enhances the efficiency of research and development; improving telehealth by creating virtual assistants that are intelligent for remote patient engagement and support; and improved data security via AI systems being expected to play a key role in safeguarding sensitive patient data, managing cloud-based data, and protecting against breaches. Potential developments for AI in healthcare include streamlined patient record review; improved resource management for increased efficiency and sustainability in healthcare systems; early disease detection via image analysis, such as radiographs and pathology slides to identify subtle patterns that humans might miss; risk factor identification; data analysis being able to create personalized treatment plans; drug discovery and design; the creation of virtual health assistants for patient guidance; pattern recognition via AI’s ability to analyze vast datasets from genomic sequencing, imaging, and wearable devices to find hidden patterns, which enables earlier disease detection and better outcomes. Ethical considerations of AI in healthcare include ensuring patient privacy and data security, addressing potential biases and discrimination in algorithms, ensuring transparency and accountability for AI decisions, obtaining proper informed consent from patients, maintaining human oversight and empathy, and establishing clear regulatory frameworks to govern AI use and mitigate errors. Taking my personal insights into account on the future of AI in healthcare, I deeply believe that AI is doing phenomenal things for healthcare workers and patients, such as reducing burdens for workers, saving patients' lives, and being preventative when it comes to diseases. In my opinion, AI is, for the most part, being used ethically, but I believe that it's important to particularly focus on the ethical considerations of ensuring that patient data is safe and private, addresing all potential biases and discrimination that were trained into AI systems, obtaining informed consent from patients for AI to be used, and erasing the lack of transparency and accountability. I believe that by focusing on these ethical considerations and addressing them, AI will continue to improve and do amazing, beneficial things for the healthcare industry and everyone affected by it.

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AI-Powered Predictive Healthcare: Inside India’s Multi-Billion-Dollar Health Tech Revolution

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AI-Powered Predictive Healthcare: Inside India’s Multi-Billion-Dollar Health Tech Revolution

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AI-Powered Predictive Healthcare: Inside India’s Multi-Billion-Dollar Health Tech Revolution

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The broader societal impacts of AI in healthcare include the risk of biased algorithms; disparities in access due to the high cost of implementing advanced AI tools, which could favor well-funded, urban healthcare systems, which leaves under resourced and rural areas behind; data privacy and consent issues due to AI systems relying on vast amounts of sensitive patient data, which raises complex issues surrounding data ownership and security; accountability and liability issues due to the “black box” nature of AI systems; transparency and trust issues due to the possibility of AI arriving at conclusions that can erode patient and provider trust; AI’s ability to increase human expertise in the workforce rather than entirely replace it via automating routine administrative tasks; new skills and roles due to the shift towards AI-enabled healthcare that will require workers to acquire new skills, such as data analysis, AI system management, and cybersecurity; and changes to the patient-provider dynamic due to AI handling repetitive tasks, which in turn allows providers to focus on the distinctly human elements of patient care, such as empathy, communication, and critical thinking. However, overreliance on AI could potentially risk the deskilling of clinicians and depersonalize the patient's experience. Societal impacts specifically on AI and drug discovery and medical research include faster drug discovery due to AI and machine learning streamlining the costly and time-consuming process by helping scientists be able to identify potential molecules and optimize clinical trial designs; precision medicine tailored to individual patients and their needs; and AI’s ability to analyze data to predict disease trends and forecast outbreaks, which equips public health officials with more effective tools for prevention and intervention. AI affects employment in healthcare by automating administrative tasks such as coding, data entry, and scheduling; job displacement due to the automation of tasks; increased intelligence, which equips AI with the ability to be "cognitive assistants" for clinicians, and their increased performance in diagnosis, treatment planning, and research, which allows professionals to turn to AI for better, faster insights; and new skill requirements for healthcare profesisonals. AI affects privacy in healthcare by increasing the risk of cyberattacks, data breaches, and the misuse of information; informed consent must be given by patients for AI to use their data; and third-party access, which involves AI sharing data with third-party developers. This leads to concerns about who owns the data and how it's protected. Ways that AI affects equity in healthcare include algorithmic bias that comes from training data; unequal access to technology due to the high cost of developing and implementing AI, which leads to underresourced healthcare systems and low-income communities missing out on the opportunity of potentially life-saving innovations; inadequate data representation due to AI models being trained on non-representative data sets and as a result, may perform poorly on underrepresented populations, leading to innacurate diagnoses or treatment plans that are suboptimal; and exacerbated systematic issues due to AI mirroring historical inequities in care, leading AI's ability to institutionalze and perpetuate existing disparities in treatment, access, and outcomes. Other relevant societal issues of AI in healthcare include accessibility and quality of care via telemedicine and virtual assistants, which expand access to care, particularly in underserved regions; accountability and transparency issues; trust issues, due to a lack of transparency; and necessary, strong regulations and governance that must be implemented to guide the ethical development and deployment of AI. Diving into my personal insights, the negative societal impacts influence my perception of AI in healthcare by helping me to understand issues that must be resolved for AI to be used more ethically in healthcare. The positive societal impacts make me perceive AI in healthcare in a positive light as a life-saving tool for patients and a burden reducer for professionals. I believe that by addressing the negative societal impacts and keeping the positive ones, AI can reach its full potential in healthcare and continue to be extremely beneficial for everyone.

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The research process was super simple. I didn’t encounter any challenges at any point throughout my research. I was able to easily identify the five sources for my citations and format them into an APA works cited post. I learned about all the ways in which AI is used in healthcare. I learned about the positive and negative societal impacts of AI in this industry; the ethical considerations; future trends; and benefits, improvements, and challenges of specific AI applications in healthcare. I found my research incredibly interesting throughout the process, and I looked forward to working on it. Overall, the only challenge I had was trying to get my three videos in the same post as the examples. I was unable to do it after researching it, so I made separate posts for them. I think that AI in healthcare is, for the most part, extremely beneficial and is doing a lot of great, life-saving things. I also believe that the ethical considerations and negative societal impacts need to be addressed for AI to be more ethical and safe in this industry. This research has influenced my perspective on AI by bringing to my attention all the ways AI can be used, including all its applications, especially in healthcare. This research also influenced my perspective by bringing to my attention ethical considerations, negative societal impacts, and challenges that are posed by AI in healthcare that need to be addressed for AI in this industry to be more ethical and safe.

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Examples of AI used in health care - st. george’s university. St. George\’s University School of Medicine. (2025, May 21). https://www.sgu.edu/school-of-medicine/blog/ai-in-medicine-and healthcare/#:~:text=AI%20systems%20have%20been%20used%20to%20analyze,like%20cancer%2C%20retinal%20diseases%2C%20pneumonia%2C%20and%20more. [Rationale: I chose this article because it provides examples of AI in healthcare that I discussed, and it relates to my description of AI being used for diagnostics in healthcare.]

Iftikhar, M., Saqib, M., Zareen, M., & Mumtaz, H. (2024, August 1). Artificial Intelligence: Revolutionizing robotic surgery: Review. Annals of medicine and surgery (2012). https://pmc.ncbi.nlm.nih.gov/articles/PMC11374272/#:~:text=Robotic%20surgery%2C%20known%20for%20its,surgeon%20fatigue%2C%20and%20improved%20safety. [Rationale: I decided to choose this article because the author clearly described how AI is used in robotic surgery and the advantages that this application has, which also supported my research on that topic.]

IMOSolutions. (2025, May 20). The future of clinical documentation is ambient, automated, and AI-powered. IMO Health. https://www.imohealth.com/resources/the-future-of-clinical-documentation-is-ambient-automated-and-ai-powered/#:~:text=A%20large%2Dscale%20study%20on,Contextual%20summarization. [Rationale: I chose this article for my citation because it backed up my research on how AI is also used in healthcare for clinician note-taking via voice recognition.]

Kokudeva, M., Vichev, M., Naseva, E., Miteva, D. G., & Velikova, T. (2024, September 20). Artificial Intelligence as a tool in drug discovery and development. World journal of experimental medicine. https://pmc.ncbi.nlm.nih.gov/articles/PMC11372739/#:~:text=Emergence%20of%20AI%20in%20drug,of%20AI%20in%20pharmaceutical%20research. [Rationale: I chose the article because the quote that I drew from it clearly explains what machine and deep learning algorithms do when it comes to drug discovery, thus enhancing my research.]

Marlene M. Maheu, P. (2025, August 10). “Ai use cases in healthcare” : A strategic AI & chatgpt guide. Telehealth.org. https://telehealth.org/blog/ai-use-cases-in-healthcare/#:~:text=For%20instance%2C%20an%20AI%2Ddriven%20system%20might%20analyze,improving%20the%20overall%20efficiency%20of%20the%20practice. [Rationale: I chose this article because the author clearly articulates how AI is used in healthcare for administrative tasks and how it improves the overall efficiency of a medical practice, thus strengthening the research I did.]