Christensen, Jen. (2023, February 3). Paging Dr. AI? What ChatGPT and artificial intelligence could mean for the future of medicine. CNN. https://www.cnn.com/2023/02/02/health/artificial-intelligence-medicine/index.html
This article looks at the general use of AI in medicine and how it could be incredibly useful for generating a diagnosis and treatment for patients. It describes how they used ChatGPT to produce quality answers to the medical examination which all doctors are required to pass. This source is useful for my project since it displays examples of it being used in a positive way and shows how while AI won’t replace doctors, it could be used as a complement.
Fornell, Dave. (2023, January 12). AI predicts heart disease risk using single chest X-ray. Radiology Business. https://radiologybusiness.com/topics/artificial-intelligence/video-ai-predicts-heart-disease-risk-using-single-chest-x-ray
This article looks at the research being performed by a Mass General doctor who is interested in the use of AI in his own practices. He is looking at how an AI system could be used to determine a patient’s cardiovascular risks based on just a chest X-ray. While this is still in development, it could be useful to support my final project as it shows that when a system is correctly and carefully manufactured it could provide incredible use to the medical field.
London A. J. (2022). Artificial intelligence in medicine: Overcoming or recapitulating structural challenges to improving patient care?. Cell reports. Medicine, 3(5), 100622. https://doi.org/10.1016/j.xcrm.2022.100622
This article discusses the structural challenges of AI and provides recommendations for how such problems may be overcome so that AI can be successful in the field of medicine. This source is useful for my project as it lays out both the current negatives as well as the endless possibilities of AI and its potential usefulness in a medical environment. I especially like this one because it provides the perspective on how to change AI to make it more useable.
McCall, Jonathan. (2023). Building Better Guardrails for Algorithmic Medicine. Duke AI Health. https://aihealth.duke.edu/building-better-guardrails-for-algorithmic-medicine/
This article describes an algorithmic tool that was being used to alert clinical staff of possible sepsis and the difficulties that researchers came across it. This source will be useful because it explains how AI really could be useful for medicine if the system is properly designed to avoid any difficulties, really emphasizing the importance of validating the system throughout the manufacturing stages.
Poon, A. I. F., & Sung, J. J. Y. (2021). Opening the black box of AI-Medicine. Journal of gastroenterology and hepatology, 36(3), 581–584. https://doi.org/10.1111/jgh.15384
This source explains the challenges with using AI in medicine and particularly the reluctance from physicians to trust and adopt algorithms that they don’t really understand. This source will be useful for my projects as I am hoping to focus in on the concerns surrounding the use of machine learning in the medical field. This will be useful in showing the additional steps that will need to be taken before AI will have a successful role in the field.
Quinn, T. P., Jacobs, S., Senadeera, M., Le, V., & Coghlan, S. (2022). The three ghosts of medical AI: Can the black-box present deliver?. Artificial intelligence in medicine, 124, 102158. https://doi.org/10.1016/j.artmed.2021.102158
This paper looks at trust surrounding AI and the skeptics of whether AI can really deliver in health care. This source is important to my project as it lays out the concerns with applying something like AI to healthcare, describing concerns like lack of quality care, trust and physician-patient dialogue. This particular paper doesn’t feel AI can successfully produce quality medicine.
Savage, Neil. (2022, March 29). Breaking into the black box of artificial intelligence. Nature. https://www.nature.com/articles/d41586-022-00858-1
This article describes how AI has already been used in medicine but goes further to explain its shortcomings particularly in its use to diagnose COVID-19 patients. This source will be useful for my project because it demonstrates how AI could be useful but also underlies the shortcuts it takes that could create unnecessary problems. This particular COVID-19 example had to do with AI drawing too many conclusions from chest x-rays that weren’t consistent in diagnosing patients.
