Hersh William
Division of Informatics, Clinical Epidemiology and Translational Science, Department of Medicine, School of Medicine, Oregon Health & Science University, Portland, Oregon, USA; email:
Annu Rev Biomed Data Sci. 2025 Apr 9. doi: 10.1146/annurev-biodatasci-103123-094756.
Generative artificial intelligence (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 to overcome the shortcomings of LLM use in education. Although there are challenges for the 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.
生成式人工智能(AI)在专业工作和教育领域对生物医学与健康都产生了深远影响。基于大语言模型(LLMs),生成式AI在模拟的医学委员会考试、回答临床问题、解决临床病例、应用临床推理以及总结信息等情境中表现得与人类一样出色。生成式AI在教育领域也得到广泛应用,在学术课程及其评估中表现良好。本综述总结了大语言模型的成功之处,并强调了它们在教育背景下的一些挑战,最显著的是可能会妨碍专业工作知识和技能获取的方面。然后提供了最佳实践建议,以克服在教育中使用大语言模型的缺点。尽管在教育中使用生成式AI存在挑战,但生物医学与健康领域及其他领域的所有学生和教师都必须了解并能够熟练使用它。