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The industry I have chosen to research is healthcare. The reason I chose this industry is because I have several family members who work in healthcare. Also, I have been on a medical leave of absence since the fall 2023 semester, and am only returning this semester. Over the past year and a half, I have spent a considerable amount of time in and out of the hospital, so suffice it to say that this industry is of great interest and very relevant to me. In this assignment, I will be discussing several things regarding AI in healthcare. I will discuss some of the ways in which AI is being used in this industry, including how it is being used in medical imaging and diagnostics, how it is being used in predictive analytics for disease prevention, and also how AI can be used to assist in robotic surgery. I will also be discussing the societal impact and future trends of AI in healthcare.

Another reason I chose to research AI in this industry is because I was curious to see the ways in which AI is being used in healthcare. As I mentioned, I have spent quite a bit of time in the hospital recently, and although the frequency with which I will be returning to the hospital has now decreased a great deal, I will still need to be back periodically throughout the rest of my life. So, I also chose this industry because I am curious how these AI applications will progress, and the effect they might have on my own treatment. I am hopeful that AI, so long as we are aware of and take precautions against the dangers, will do a lot of good in the medical world, both for clinicians and patients alike.

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AI is being used to enhance and analyze medical imaging, and to improve the accuracy of diagnoses. I think this is especially helpful to those who have been struggling with symptoms that doctors can’t make sense of. This application uses machine learning, especially deep learning with algorithms like convolutional neural networks, to analyze medical images and to detect diseases. Computer vision and Generative Adversarial Networks are also used to interpret and enhance images. The benefits of this application include early disease detection, due to AI’s ability to recognize patterns that might be missed by humans, more accurate diagnoses, and personalized treatment plans. These are incredible benefits, but there are some challenges and limitations. Due to AI’s dependence on large datasets, bias is an implication. AI’s are also generally considered black boxes, we don’t really understand how they work or why they make the choices they make, so another challenge is this - doctors can have difficulty interpreting and understanding the decisions made by AI.

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Medical Imaging: From X rays to AI Assisted Diagnosis

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AI can be used to analyze health records to predict disease and progression of said disease, to optimize treatment plans, and to improve patients’ recovery rates. To identify risk factors and predict outcomes, AI uses machine learning and deep learning. AI also uses Natural Language Processing to help it to analyze clinical notes and unstructured data. The benefits of this application include early detection of disease, and personalized medicine and treatment plans. AI can also help doctors make decisions due to its predictive abilities. The challenges and limitations are mostly the same as previously mentioned, and as always when using AI, there are ethical concerns including bias and data privacy. Another challenge to mention, however, is that although incredibly useful, adopting AI applications into existing healthcare is a complicated matter due to the complexity of healthcare. I think using AI predictively in healthcare is an interesting and important application because, typically, the earlier disease is detected, the more treatable it is.

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How Predictive Analytics Can Transform Healthcare Sector

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AI can assist in robotic surgeries by automating certain steps, using sensors to improve tactile measurements allowing surgeons to better control and interact with tissues, and can help train surgeons with real-time feedback and skill assessments. AI uses machine learning to improve the autonomy and precision of robotic surgeries, and computer vision to provide real-time tissue recognition and feedback. Benefits include improved recovery and outlook for patients, more precise movements which reduces errors, and better trained surgeons from the feedback provided during surgery. Challenges and limitations include those listed before, but another challenge is with how unpredictable surgery can be. Although better detected with AI assistance, if a different approach to the surgery is needed (expanding surgery site, switching to an open procedure, etc.), the changes can be difficult. I think this is one of the most important applications of AI within healthcare. While I was extremely lucky and had an incredibly talented and experienced neurosurgeon when I needed it, the ability to have AI assist in surgery could improve outcomes for every patient.

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In focus: The future of surgery with medical robotics and AI

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As AI advances, there will be new developments in its applications, including within healthcare. Although implementing AI into healthcare is a complicated matter due to the complexities of healthcare and healthcare systems, if done correctly, there could be some wonderful developments. Some of these center around improving efficiency. With AI assisting, things like documentation could be automated and completed quickly, allowing for more focus on patient care. AI has started to be used to detect disease, and will keep improving at this, significantly decreasing the time it takes to diagnose a patient which increases their outcomes and outlooks. AI could also suggest and improve treatment plans, making them more personalized, and once again improving the outcomes and outlooks for patients.

There are many ethical considerations when it comes to having AI in healthcare. As I seem to keep mentioning, and probably always will due to the way AI systems are created and trained with large datasets, bias is an issue. This is a particularly important consideration in healthcare, however, because such biases could lead to missing a diagnosis, or someone being misdiagnosed, both things with extreme and potentially fatal consequences. From a less life or death standpoint, not being diagnosed or being misdiagnosed is something that I know can harm people emotionally, not just physically, so I think, while bias is always an issue when using AI, it is especially poignant in healthcare. Another ethical consideration is the privacy and security of data, as well as making sure patients are informed on how AI is being used in their treatment. When dealing with medical problems, being informed is something I have found goes a long way to calm someone down. AI should also always be, but especially in healthcare, transparent and explainable, and there needs to be a way to determine accountability.

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The societal impacts of AI in healthcare are mostly broad and true for multiple industries. AI can run and automate things like administrative tasks and diagnostics, so the need for humans to do these tasks, which are a part of healthcare services, will decrease over time. The reduction of need for certain jobs is also true about basically any industry that AI is being implemented in, and will continue to be implemented in. While AI will reduce the need for certain jobs, there will be a new demand for jobs to manage and oversee AI. In terms of equity, the potential for bias within AI could have the negative impact of reinforcing healthcare disparities. The cybersecurity risks involved in allowing AI access to sensitive patient information is a potentially dangerous societal impact. On a more positive note, AI being used in healthcare could increase the accessibility of healthcare through things such as telemedicine driven by AI.

When it comes to AI, there will always be issues to consider. I do not believe that the negative societal impacts of AI in healthcare are necessarily different to those when AI is used in any other industries. The only real difference is the consequences of such societal impacts. For example, privacy is generally always a concern with AI, but the consequences of privacy being breached in healthcare is different than in other industries such as finance. In my opinion, this just means that we must be careful when implementing AI, in any industry, and do what we can to avert the potential negative societal impacts.

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During my research I learned about several of the ways in which AI is being used within healthcare. I decided to focus on medical imaging and diagnostics, predictive analytics for disease prevention, and AI-Assisted robotic surgery. I discovered how exactly AI is being used in these examples, how it might improve in the future, and things to take into consideration about its application. I think AI could be a wonderful tool within healthcare, and help to save and improve lives. Some challenges I faced while researching were choosing which three examples of AI use I wanted to focus on because there’s a ton to choose from, and finding good sources was a bit of a challenge until I found the National Library of Medicine, where I had almost all the information I needed for this assignment. One thing I’d never considered but makes a lot of sense is how difficult AI is to be implemented in healthcare because of how complicated and complex healthcare systems can be. If AI is able to be successfully implemented in healthcare, I believe that the benefits of its use far outweigh the potential dangers that come along with it.

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Buch, Varun H, et al. “Artificial Intelligence in Medicine: Current Trends and Future Possibilities.” National Library of Medicine, Royal College of General Practitioners, Mar. 2018, https://pmc.ncbi.nlm.nih.gov/articles/PMC5819974/. Accessed 9 Mar. 2025.

(Rationale: this source provides information on the future trends of AI in healthcare)

Dixon, Diny, et al. “Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review.” Edited by Alexander Muacevic and John R. Adler, National Library of Medicine, Cureus Inc., 9 May 2024, https://pmc.ncbi.nlm.nih.gov/articles/PMC11161909/#:~:text=AI%20predictive%20analytics%20leverages%20advanced,improve%20over%20time%20%5B6%5D. Accessed 8 Mar. 2025.

(Rationale: Provides overview of AI use in predictive analysis for disease prevention within healthcare)

Farhud, Dariush  D, and Shaghayegh Zokaei. “Ethical Issues of Artificial Intelligence in Medicine and Healthcare.” National Library of Medicine, Tehran University of Medical Sciences, Nov. 2021, https://pmc.ncbi.nlm.nih.gov/articles/PMC8826344/. Accessed 9 Mar. 2025.

(Rationale: discusses the ethical considerations of AI in healthcare)

Knudsen, J  Everett, et al. “Clinical Applications of Artificial Intelligence in Robotic Surgery.” National Library of Medicine, Springer, 1 Mar. 2024, https://pmc.ncbi.nlm.nih.gov/articles/PMC10907451/. Accessed 8 Mar. 2025.

(Rationale: Provides overview of AI-assisted robotic surgeries, including how it is used and the technology used)

Pinto-Coelho, Luís. “How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications.” Edited by Seung Won Lee, National Library of Medicine, Multidisciplinary Digital Publishing Institute, 18 Dec. 2023, https://pmc.ncbi.nlm.nih.gov/articles/PMC10740686/. Accessed 8 Mar. 2025.

(Rationale: this source provides the explanation, benefits, challenges, and technology used for AI in medical imaging and diagnostics)

Rahman, Md  Ashrafur, et al. “Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin.” National Library of Medicine, 22 Jan. 2024, https://pmc.ncbi.nlm.nih.gov/articles/PMC10804900/. Accessed 9 Mar. 2025.

(Application: Provides an overview of the societal impacts at AI in healthcare)

“Robotic Surgery: The Future Is Here.” Cleveland Clinic, Cleveland Clinic, 30 Apr. 2024, my.clevelandclinic.org/health/treatments/22178-robotic-surgery.

(Rationale: This source is about AI-assisted robotic surgeries, but elaborates more on the benefits and challenges)