Generative Artificial Intelligence: Implications for Biomedical and Health Professions Education
It addresses the integration of generative AI into education for students and faculty in biomedicine and health, though it is incremental as it reviews existing successes and challenges without introducing new methods.
This review examines the impact of generative AI, particularly large language models (LLMs), on biomedical and health professions education, highlighting that LLMs perform as well as humans in tasks like medical board exams and clinical reasoning, while also addressing challenges such as undermining skill acquisition and providing recommendations for best practices.
Generative AI has had a profound impact on biomedicine and health, both in professional work and in education. Based on large language models (LLMs), generative AI has been found to perform as well as humans in simulated situations taking medical board exams, answering clinical questions, solving clinical cases, applying clinical reasoning, and summarizing information. Generative AI is also being used widely in education, performing well in academic courses and their assessments. This review summarizes the successes of LLMs and highlights some of their challenges in the context of education, most notably aspects that may undermines the acquisition of knowledge and skills for professional work. It then provides recommendations for best practices overcoming shortcomings for LLM use in education. Although there are challenges for use of generative AI in education, all students and faculty, in biomedicine and health and beyond, must have understanding and be competent in its use.