• Daniel J Parente.
• Department of Family Medicine and Community Health, University of Kansas Medical Center, Kansas City, KS.
• Fam Med. 2024 Oct 1; 56 (9): 534540534-540.

AbstractGenerative artificial intelligence and large language models are the continuation of a technological revolution in information processing that began with the invention of the transistor in 1947. These technologies, driven by transformer architectures for artificial neural networks, are poised to broadly influence society. It is already apparent that these technologies will be adapted to drive innovation in education. Medical education is a high-risk activity: Information that is incorrectly taught to a student may go unrecognized for years until a relevant clinical situation appears in which that error can lead to patient harm. In this article, I discuss the principal limitations to the use of generative artificial intelligence in medical education-hallucination, bias, cost, and security-and suggest some approaches to confronting these problems. Additionally, I identify the potential applications of generative artificial intelligence to medical education, including personalized instruction, simulation, feedback, evaluation, augmentation of qualitative research, and performance of critical assessment of the existing scientific literature.

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