The following journal articles, organizational resources, and technical references support the Principles for the Responsible Use of Artificial Intelligence in and for Medical Education developed by the AAMC.
Additional Relevant Journal Articles
- Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. 2019;5(1):e13930. doi:10.2196/13930
- Gordon M, Daniel M, Ajiboye A, et al. A scoping review of artificial intelligence in medical education: BEME Guide no. 84. Med Teach. 2024;46(4):446-470. doi:10.1080/0142159X.2024.2314198
- Knopp MI, Warm EJ, Weber D, et al. AI-enabled medical education: threads of change, promising futures, and risky realities across four potential future worlds. JMIR Med Educ. 2023;9:e50373. doi:10.2196/50373
- Nagi F, Salih R, Alzubaidi M, et al. Applications of artificial intelligence (AI) in medical education: a scoping review. Stud Health Technol Inform. 2023;305:648-651. doi:10.3233/shti230581
- Sun L, Yin C, Xu Q, Zhao W. Artificial intelligence for healthcare and medical education: a systematic review. Am J Transl Res. 2023;15(7):4820-4828.
- Triola, MM, Rodman A. Integrating Generative artificial intelligence into medical education: curriculum, policy, and governance strategies. Acad Med. Published online December 20, 2024. doi:10.1097/ACM.0000000000005963
- Xu X, Chen Y, Miao J. Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review. J Educ Eval Health Prof. 2024;21:6. doi:10.3352/jeehp.2024.21.6
Organizational Resources
- AAMC. Principles for responsible AI in medical school and residency selection. Published July 2024. Accessed December 30, 2024. https://www.aamc.org/about-us/mission-areas/medical-education/principles-ai
- Department of Education Office of Educational Technology. Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations (PDF). Published May 2023. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf
- Lomis K, Jeffries P, Palatta A, et al. Artificial intelligence for health professions educators. NAM Perspect. 2021;2021:10.31478/202109a. doi: 10.31478/202109a
- National Academies of Sciences, Engineering, and Medicine. Artificial Intelligence in Health Professions Education: Proceedings of a Workshop. National Academies Press; 2023. doi:10.17226/27174
Technical References
- Association for Computing Machinery. FAccT ’23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. 2023. https://dl.acm.org/doi/proceedings/10.1145/3593013
- Binns R. Fairness in machine learning: lessons from political philosophy. Proc Mach Learn Res. 2018;18:149-159. https://proceedings.mlr.press/v81/binns18a.html
- Black NB, George S, Eguchi A, et al. A framework for approaching AI education in educator preparation programs. Proc AAAI Conf Artif Intell. 2024;38(21):23069-23077. doi:10.1609/aaai.v38i21.30351
- Hao J, von Davier AA, Yaneva V, Lottridge S, von Davier M, Harris DJ. Transforming assessment: the impacts and implications of large language models and generative AI. Educ Meas Issues Pract. 2024;43(2):16-29. doi:10.1111/emip.12602
- Kochmar E, Bexte M, Burstein J, et al, eds. BEA 24: Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics; 2024. https://aclanthology.org/2024.bea-1
- Lalor J, Yang Y, Smith K, Forsgren N, Abbasi A. Benchmarking intersectional biases in NLP. In: Carpuat M, de Marneffe MC, Ruiz IVM, eds. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics; 2022:3598-3609. doi:10.18653/v1/2022.naacl-main.263
- Muktha A, Malik DB, Burstein J, et al. AI for education at AAAI 2024: bridging innovation and responsibility. Proc Mach Learn Res. 2024;257:1-2. https://proceedings.mlr.press/v257/ananda24a.html
- Naumann T, Ben Abacha A, Bethard S, Roberts K, Bitterman D, eds. Proceedings of the 6th Clinical Natural Language Processing Workshop. Association for Computational Linguistics; 2014. https://aclanthology.org/volumes/2024.clinicalnlp-1/
- Rust P, Søgaard A. Differential privacy, linguistic fairness, and training data influence: impossibility and possibility theorems for multilingual language models. Proc Mach Learn Res. 2023;202:29354-29387. https://proceedings.mlr.press/v202/rust23a