Demystifying Artificial Intelligence in Rheumatology

Shaping Our Own Future

As AI continues to evolve, it is imperative that rheumatologists play an active role in shaping AI’s integration into the field. We can neither be swept up by the overenthusiastic promises of AI marketeers nor ignore the very real and tangible good that technological advances can provide our patients. We must be careful and deliberative with our adoption of technologies, such as AI, in an age that values the next shiny penny. Only by engaging in advocacy and dialogue with policymakers and technocrats can we ensure the development of AI technologies align with the foundational values we cherish in the field of rheumatology, particularly the emphasis on patient-centered care.

Rheumatologists must also stay informed about the advancements in AI to use these technologies effectively in our practice. It’s not an exaggeration to say that we need to embrace computer science and programming as a competency in our fellowship programs and offer continuing medical education programs in informatics. This proactive approach will ensure that AI is used as a tool to enhance, and not commodify, the nuanced art of rheumatology.

In short, AI is advancing at a rapid pace, but it can never catch up to genuine human wisdom. In the ideal world, we will harmonize artificial intelligence with genuine human wisdom, and, in the process, advance the art and the science of rheumatology. Until that happens, I’ll keep getting ads for artificially intelligent bird feeders.

Bharat Kumar, MD, MME, FACP, FAAAAI, RhMSUS, is the director of the rheumatology fellowship training program at the University of Iowa, Iowa City, and the physician editor of The Rheumatologist. Follow him on X (formerly Twitter) @BharatKumarMD.

References

1. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019 Apr;28(2):73–81.
2. Toosi A, Bottino AG, Saboury B, et al. A brief history of AI: How to prevent another winter (A critical review). PET Clin. 2021 Oct;16(4):449–469.
3. De Togni G, Erikainen S, Chan S, et al. Beyond the hype: ‘Acceptable futures’ for AI and robotic technologies in healthcare. AI Soc. 2023 Apr:1–10.
4. Ammenwerth E, Spötl HP. The time needed for clinical documentation versus direct patient care. A work-sampling analysis of physicians’ activities. Methods Inf Med. 2009;48(1):84–91.
5. Committee on Diagnostic Error in Health C, Board on Health Care S, Institute of M, The National Academies of Sciences E, Medicine. Balogh EP, Miller BT, Ball JR, eds. Improving diagnosis in health care. Washington (DC): National Academies Press. 2015.
6. Wornow M, Xu Y, Thapa R, et al. The shaky foundations of large language models and foundation models for electronic health records. NPJ Digit Med. 2023 Jul;6(1):135.
7. Perry A. AI will never convey the essence of human empathy. Nat Hum Behav. 2023 Nov;7(11):1808–1809.
8. Park PS, Goldstein S, O’Gara A, et al. AI deception: A survey of examples, risks, and potential solutions. Patterns (N Y). 2024 May;5(5):100988.