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Exploring the Role of Artificial Intelligence in Rheumatology

Bharat Kumar, MD, MME, FACP, FAAAAI, RhMSUS  |  Issue: November 2022  |  November 4, 2022

I confess: When I initially read how the joints whispered to the clinician that they needed more humanism, I thought “What clichéd nonsense is this?” But the more I thought about it, the more I realized that the machine actually proposed the only path for rheumatology professionals to balance the risks and benefits of AI: We have to wholeheartedly embrace our own humanism. At this point in time, we need to ensure the greater efficiency provided by AI will afford us more agency to be humanistic to those whispering joints—and the humans that use them.

Altogether, this means we must prioritize our engagement with AI. Important first steps include increasing funding for research on informatics, artificial intelligence and machine learning, bolstering information technology support for clinical divisions, training fellows to familiarize themselves with AI and scrutinizing quality improvement work to ensure it embraces the principles of human-centered design. Moreover, we have to do this promptly, or events may swiftly overtake us. This is exemplified by the last, seemingly ominous, words of that AI-generated Rheuminations column:

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The joints were happy and satisfied. Humanism prevailed, at least for the day.


Bharat Kumar, MDGuest columnist Bharat Kumar, MD, MME, FACP, FAAAAI, RhMSUS, is the associate program director of the rheumatology fellowship training program at the University of Iowa, Iowa City. He will be assuming the reins of physician editor of The Rheumatologist from Philip Seo, MD, MHS, with the January 2023 issue. Follow him on Twitter @BharatKumarMD.

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References

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  5. DALL·E mini – by Craiyon.com. https://huggingface.co/spaces/dalle-mini/dalle-mini. Accessed July 8, 2022.
  6. Kusters R, Misevic D, Berry H, et al. Interdisciplinary research in artificial intelligence: challenges and opportunities. Front Big Data. 2020 Nov 23;3:577974.
  7. Aggarwal R, Ringold S, Khanna D, et al. Distinctions between diagnostic and classification criteria? Arthritis Care Res (Hoboken). 2015 Jul;67(7):891–897.
  8. De Cock D, Myasoedova E, Aletaha D, et al. Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs). Ther Adv Musculoskelet Dis. 2022 Jun 30;14:1759720X221105978.
  9. Duong SQ, Crowson CS, Athreya A, et al. Clinical predictors of response to methotrexate in patients with rheumatoid arthritis: A machine learning approach using clinical trial data. Arthritis Res Ther. 2022 Jul 1;24(1):162.
  10. Deodhar A, Rozycki M, Garges C, et al. Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis. Clin Rheumatol. 2020 Apr;39(4):975–982.
  11. Beltramin D, Lamas E, Bousquet C. Ethical issues in the utilization of black boxes for artificial intelligence in medicine. Stud Health Technol Inform. 2022 Jun 29;295:249–252.
  12. Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare. 2020:295–336.
  13. Battafarano DF, Ditmyer M, Bolster MB, et al. 2015 American College of Rheumatology workforce study: Supply and demand projections of adult rheumatology workforce, 2015–2030. Arthritis Care Res (Hoboken). 2018 Apr;70(4):617–626.
  14. Meskó B, Hetényi G, Győrffy Z. Will artificial intelligence solve the human resource crisis in healthcare? BMC Health Serv Res. 2018 Jul 13;18(1):545.
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  16. Hut N. Revamping prior authorization: How AI and automation could boost care and revenue. Healthcare Financial Management Association. 2021 Oct 5. https://www.hfma.org/topics/technology/article/revamping-prior-authorization-how-ai-and-automation-could-boost-care-and-revenue.html.

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