Artificial Intelligence in Health

"The future of Artificial Intelligence in Medicine (AIM) is bright, building on the remarkable transformation in technology, computing, medicine, and biology over the past half-century." [1]

With the wealth of big data and advancements in technology has come the rapid growth of artificial intelligence (AI) in health. From its earliest days, in the 1960s and 1970s, AI promised to positively impact the many facets of health and health care [2,3]. Through the 1980s and 1990s the phenomenon of “AI Winter” was experienced as the potential for AI was seen as limited, largely due to computational capacity. Since the early 2000s, we have seen an “AI Summer” emerge, with major advances demonstrating the potential for AI to efficiently translate language, win at complex games such as chess, and engage in conversations with efficient ability to retrieve and synthesize volumes of information (e.g., OpenAI’s ChatGPT).

AI methods such as machine learning and natural language processing can be used to discover new insights for disease diagnosis, treatment, and prevention from large amounts of disparate data such as those from electronic health record (EHR) systems [4,5]. These insights can then be implemented as AI-based solutions such as clinical decision support tools in EHR systems. However, there are a range of challenges for ensuring rigorous, reproducible, and responsible development, implementation, maintenance, and use of AI in healthcare settings [6,7].

In considering the ways that AI can be used to improve health care, key stakeholders (e.g., patients and their caregivers, clinicians, care coordination managers, clinical business leadership, and researchers) need to be engaged throughout the development process, from design to implementation to evaluation [8].

See CODIAC for Health chapter on Artificial Intelligence in Health (forthcoming) for more information.

References

  1. Shortliffe EH. Artificial Intelligence in Medicine: Weighing the Accomplishments, Hype, and Promise. Yearb Med Inform. 2019 Aug;28(1):257-262. doi: 10.1055/s-0039-1677891. Epub 2019 Apr 25. PMID: 31022745; PMCID: PMC6697517.

  2. Patel VL, Shortliffe EH, Stefanelli M, Szolovits P, Berthold MR, Bellazzi R, Abu-Hanna A. The coming of age of artificial intelligence in medicine. Artif Intell Med. 2009 May;46(1):5-17. doi: 10.1016/j.artmed.2008.07.017. Epub 2008 Sep 13. PMID: 18790621; PMCID: PMC2752210.

  3. Shortliffe EH. The adolescence of AI in medicine: will the field come of age in the '90s? Artif Intell Med. 1993 Apr;5(2):93-106. doi: 10.1016/0933-3657(93)90011-q. PMID: 8358494.

  4. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018 Oct;2(10):719-731. doi: 10.1038/s41551-018-0305-z. Epub 2018 Oct 10. PMID: 31015651.

  5. Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 2019 Apr 4;380(14):1347-1358. doi: 10.1056/NEJMra1814259. PMID: 30943338.

  6. Shortliffe EH, Sepúlveda MJ. Clinical Decision Support in the Era of Artificial Intelligence. JAMA. 2018 Dec 4;320(21):2199-2200. doi: 10.1001/jama.2018.17163. PMID: 30398550.

  7. Shortliffe EH. Role of evaluation throughout the life cycle of biomedical and health AI applications. BMJ Health Care Inform. 2023 Dec 11;30(1):e100925. doi: 10.1136/bmjhci-2023-100925. PMID: 38081766; PMCID: PMC10729087.

  8. Li RC, Asch SM, Shah NH. Developing a delivery science for artificial intelligence in healthcare. NPJ Digit Med. 2020 Aug 21;3:107. doi: 10.1038/s41746-020-00318-y. PMID: 32885053; PMCID: PMC7443141.

Resources

Books/Chapters

Articles

  • Goldberg CB, Adams L, Blumenthal D, et al. To do no harm - and the most good - with AI in health care. Nat Med. 2024 Mar;30(3):623-627. doi: 10.1038/s41591-024-02853-7. PMID: 38388841.

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