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I chose to explore the use of AI technologies in healthcare settings because it is the field I am currently employed in and the area in which I am pursuing my degree in healthcare management. I work in a geriatric neuropsychiatric specialty practice, where I see firsthand how AI is being integrated into daily operations. For example, providers at my workplace use a program called Nextvisit which helps create documentation for completed appointments. Experiencing this program in use on a daily basis has given me a clear view of both the strengths and shortcomings of AI in healthcare.

My interest in this topic comes from my desire for a greater understanding of how these tools can support both patients and providers. AI has the potential to improve efficiency, reduce provider burnout, and expand and refine diagnostic capabilities. Unfortunately at times AI does not always perform as effectively or seamlessly as expected. Through my research of different applications of AI in healthcare I was actually able to compare my personal experiences to broader trends in the industry. This industry is significant to me because healthcare impacts everyone, and I believe AI has the potential to reshape it in ways that will define the future of medicine.

On my Padlet wall, I will focus on three main and prominent applications of AI in healthcare: diagnostic radiology, surgical assistance, and medical scribe services. Each of these areas demonstrates different ways AI is transforming the industry, while also shining light on the challenges surrounding accuracy, ethics, and patient safety.

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AI is being used in diagnostic radiology to help clinicians interpret medical images such as X-rays, CT scans, MRIs, and mammograms through machine learning and deep learning methods. “ AI can reduce the risk of human errors and provide more accurate results in less time. In the future, AI technology could be used to support medical decisions by providing clinicians with real-time assistance and insights…AI can also help identify abnormalities, detect fractures, tumors, or other conditions, and provide quantitative measurements for faster and more accurate medical diagnosis.” (Alowais et al., 2023). While AI will not be employed in place of a human radiologist, it is serving as a valuable tool in diagnostic imaging and has the potential to cut down on human error, leading to better outcomes for patients. AI also allows for faster diagnosis to be made, improved accuracy, reduced costs, and essentially is lending an extra hand to overburdened radiologists specifically in high volume settings like emergency rooms. AI offers promising benefits in radiology, but it comes with limitations. Some of the biggest challenges are ensuring that it is trained on high quality data, protection of patient privacy, and preventing algorithmic bias (Alowais et al., 2023). AI systems still require human oversight and expertise to validate outputs, and until the aforementioned barriers are addressed, AI should be used as a supportive tool rather than a replacement for trained clinicians.

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Clinical documentation has been a long-standing contributing factor to clinician burnout. “Time spent outside normal hours—often called “pajama time”—completing notes, orders and reviews has steadily eroded physician satisfaction and interfered with patient relationships.” (AMA, 2025). Using AI as a tool to streamline this process can be transformative. Programs like NextVisit, which is actively used in my workplace, employ natural language processing to record patient-provider conversations and automatically generate SOAP notes. According to the AMA, AI scribes have saved clinicians more than 15,000 hours of documentation time in one year, allowing them to focus on patients. While accuracy, patient privacy, and integration remain challenges, I find this application significant because I’ve seen how it can reduce burnout and restore the human side of medicine. In my experience the clinical notes produced by NextVisit are sufficient but do fall short in some ways. The program does make errors, and will oftentimes translate patient demographics incorrectly citing the name of a patient’s family member who was present for the appointment instead of the patient. The program has also glitched, and deleted or not saved notes, requiring the provider to dictate the note themselves. As for patient privacy, there are significant risks associated with using AI scribe tools, many AI systems are trained using data scraping and large language models, meaning that sensitive health information could be at risk if not handled with strict safeguards. If patient notes are transmitted to external servers there is a serious risk of unauthorized access, breaches, and HIPAA protected information being used to further train AI models. As the use of AI scribing services becomes more common in clinical practice, constant human oversight will continue to be required to ensure that the programs are maintaining accuracy and are following strict HIPAA compliance guidelines.

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Advances in AI robotics are changing the way that surgeons perform operations. AI tools are being used to assist surgeons with intraoperative tasks such as tissue recognition, instrument tracking, and precision cutting, helping with fatigue and improving accuracy during lengthy and complex procedures (Knudsen et al., 2024). The most commonly used AI powered surgical robot is the da Vinci system which follows a “master-slave” model where the surgeon controls movements from a console, while the robot performs them within the patient. The da Vinci robot cannot act autonomously, it requires human operation. Autonomy in surgery exists on a spectrum, from no autonomy at all to full independence, and many current innovations fall somewhere in between. Researchers are exploring ways AI can support decision-making in real time, such as identifying safe cutting margins, reducing errors, and standardizing outcomes across surgical teams  (Knudsen et al., 2024) . These developments could expand access to high-quality surgical care, but they also raise questions about accountability and patient trust.

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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The healthcare field is at very little risk of being completely overtaken by AI, but it’s clear that AI will continue to become a more powerful and integrated tool across all aspects of care. From diagnostic imaging to surgical assistance and documentation, AI is transforming how providers work, communicate, and deliver treatment. This is both exciting and concerning. While AI can reduce burnout, improve accuracy, and save time, it also raises serious ethical questions about data privacy, accountability, and bias. Because AI learns through deep learning and large datasets, patient information could be at risk if strong safeguards aren’t in place. Human oversight remains essential, not only to verify AI’s accuracy but to protect the empathy and judgment that technology can’t replicate. There are simply some things that no program will ever replace, like the human connection between patient and provider. Still, I believe the potential for AI in healthcare is nearly limitless when used responsibly, enhancing not replacing the people who make medicine what it is.

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While AI has improved efficiency, accuracy, and accessibility in medical settings, it also presents new societal challenges that cannot be ignored. Employment is one major concern because AI tools such as medical scribes, diagnostic imaging programs, and surgical assistance robots have begun to take over tasks once performed by humans. Although AI will likely never completely replace healthcare professionals, certain roles may become less necessary, changing the job landscape within the field.

Privacy is another pressing issue. Since AI systems learn from massive datasets, including patient information, the risk of data breaches or misuse is very real. Without strong safeguards and accountability, sensitive information could be exposed or used to train systems without patient consent. This raises both ethical and legal concerns for patients and providers.

AI also has the potential to either reduce or reinforce inequities in healthcare. When algorithms are trained on biased or incomplete data, they can produce discriminatory outcomes that disproportionately affect vulnerable populations. On the other hand, when used responsibly, AI can help close gaps in access to care by improving diagnostic accuracy and enabling telehealth solutions for underserved communities.

I see AI’s societal impact as a double-edged sword that can both improve healthcare delivery and create new risks. The key is ensuring it is developed and used with transparency, fairness, and human oversight so that it enhances, rather than replaces, the compassion and judgment that define quality care.

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Researching the use of AI in healthcare helped me see just how deeply technology is shaping the future of medicine. Because I already work in a neuropsychiatric practice that uses NextVisit, this project gave me a broader perspective beyond my day-to-day experiences. I learned how AI supports providers through tools like diagnostic imaging, robotic surgery, and scribing software while also raising major concerns about ethics, privacy, and accuracy. The biggest challenge was sorting through complex studies and balancing the pros and cons. Overall, this research strengthened my belief that AI should enhance, not replace the human element in healthcare.

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Pinto-Coelho, L. (2023). How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. Bioengineering (Basel), 10(12), 1435. https://doi.org/10.3390/bioengineering10121435 [Rationale: This article is from a trusted source and gives a great overview of the different uses of AI in diagnostic imaging.]

Nextvisit, Inc. (2025). Meet Nextvisit: Transforming Healthcare with Powerful AI. Retrieved October 5, 2025, from https://get.nextvisit.app/   [Rationale: This is the program used at my place of work, included in citations to give readers the opportunity to further explore this program]

Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., Al Yami, M. S., Al Harbi, S., & Albekairy, A. M. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23, Article 689. https://doi.org/10.1186/s12909-023-04698-z [Rationale: Used in my research as a point to refer back to, and includes overall stats of AI use in healthcare in many different sects of healthcare. Covered most of the topics I spoke about in my research.]

Johnson & Johnson. (2025, August 25). 6 ways Johnson & Johnson is using AI to help advance healthcare. Retrieved October 5, 2025, from https://www.jnj.com/innovation/artificial-intelligence-in-healthcare? [Rationale: Another article that covered the major research topics of my Padlet post, gave clear cut data sets and was used to help curate my research topics.]

American Medical Association. (n.d.). AI scribes save 15,000 hours and restore the human side of medicine. Retrieved October 5, 2025, from https://www.ama-assn.org/practice-management/digital-health/ai-scribes-save-15000-hours-and-restore-human-side-medicine [Rationale: Another peer reviewed article that covered one of my three research topics effectively, accurate data that also discussed the pros and cons of AI medical scribe programs.]

Knudsen, J. E., Ghaffar, U., Ma, R., & Hung, A. J. (2024). Clinical applications of artificial intelligence in robotic surgery. Journal of Robotic Surgery, 18(1), 102. https://doi.org/10.1007/s11701-024-01867-0 pmc.ncbi.nlm.nih.gov [Rationale: I often look for NIH articles as they are peer reviewed and reputable. This one was the basis of my research for AI in surgical settings and covered the da Vinci robot.]