Technologies, opportunities, challenges, and future directions for integrating generative artificial intelligence into medical education: a narrative review

Abstract

Generative artificial intelligence (GenAI), including large language models such as GPT-4 and image-generation tools like DALL-E, is rapidly transforming the landscape of medical education. These technologies present promising opportunities for advancing personalized learning, clinical simulation, assessment, curriculum development, and academic writing. Medical schools have begun incorporating GenAI tools to support students' self-directed study, design virtual patient encounters, automate formative feedback, and streamline content creation. Preliminary evidence suggests improvements in engagement, efficiency, and scalability. However, GenAI integration also introduces substantial challenges. Key concerns include hallucinated or inaccurate content, bias and inequity in artificial intelligence (AI)-generated materials, ethical issues related to plagiarism and authorship, risks to academic integrity, and the potential erosion of empathy and humanistic values in training. Furthermore, most institutions currently lack formal policies, structured training, and clear guidelines for responsible GenAI use. To realize the full potential of GenAI in medical education, educators must adopt a balanced approach that prioritizes accuracy, equity, transparency, and human oversight. Faculty development, AI literacy among learners, ethical frameworks, and investment in infrastructure are essential for sustainable adoption. As the role of AI in medicine expands, medical education must evolve in parallel to prepare future physicians who are not only skilled users of advanced technologies but also compassionate, reflective practitioners.

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