Human-computer interaction in healthcare
EP. 06
We’re back! This is the start of our regular discussions about healthcare AI topics and recent literature. On today’s episode - the 10 commandments of decision support, the checkered history of EMRs, clinicians as “moral crumple zone” for AI models and much more.
01:18 Technical update (Llama 3.1, Phi releases)
06:25 Main discussion
41:30 Article round-up
Some resources and papers we discuss:
Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, Spurr C, Khorasani R, Tanasijevic M, Middleton B. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc. 2003 Nov-Dec;10(6):523-30. doi: 10.1197/jamia.M1370. Epub 2003 Aug 4. PMID: 12925543; PMCID: PMC264429.
Bapna, M, Miller, K, Ratwani, RM, Electronic health record “gag clauses” and the prevalence of screenshots in peer-reviewed literature, Journal of the American Medical Informatics Association, Volume 30, Issue 10, October 2023, Pages 1717–1719, https://doi.org/10.1093/jamia/ocad138
Yu F, Moehring A, Banerjee O, Salz T, Agarwal N, Rajpurkar P. Heterogeneity and predictors of the effects of AI assistance on radiologists. Nat Med. 2024 Mar;30(3):837-849. doi: 10.1038/s41591-024-02850-w. Epub 2024 Mar 19. PMID: 38504016; PMCID: PMC10957478.
Jacobs, M., Pradier, M.F., McCoy, T.H. et al. How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection. Transl Psychiatry 11, 108 (2021). https://doi.org/10.1038/s41398-021-01224-x
Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1, 206–215 (2019). https://doi.org/10.1038/s42256-019-0048-x
Fogliato, Riccardo et al. “Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging.” Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (2022): n. pag. https://arxiv.org/abs/2205.09696
Smith, H., Birchley, G., & Ives, J. (2024). Artificial intelligence in clinical decision-making: Rethinking personal moral responsibility. Bioethics, 38, 78–86. https://doi.org/10.1111/bioe.13222
Umerenkov, D., Zubkova, G., & Nesterov, A. (2023). Deciphering Diagnoses: How Large Language Models Explanations Influence Clinical Decision Making. ArXiv, abs/2310.01708.
Huang JJ, Channa R, Wolf RM, Dong Y, Liang M, Wang J, Abramoff MD, Liu TYA. Autonomous artificial intelligence for diabetic eye disease increases access and health equity in underserved populations. NPJ Digit Med. 2024 Jul 22;7(1):196. doi: 10.1038/s41746-024-01197-3. Erratum in: NPJ Digit Med. 2024 Aug 23;7(1):220. doi: 10.1038/s41746-024-01229-y. PMID: 39039218; PMCID: PMC11263546.
Abramoff, M. D., & Char, D. (2024). What Do We Do with Physicians When Autonomous AI-Enabled Workflow is Better for Patient Outcomes? The American Journal of Bioethics, 24(9), 93–96.
NEJM AI Podcast: No doctor needed? Dr Michael Abramoff on the potential of autonomous AI
Poulain, Raphael et al. “Bias patterns in the application of LLMs for clinical decision support: A comprehensive study.” ArXiv abs/2404.15149 (2024): n. pag. https://arxiv.org/abs/2404.15149
Differences between nursing documentation for racial groups was from an oral presentation at AMIA 2023, part of the CONCERN Study