Generative AI and LLMs in Clinical Education (2026): A Practical Review for Medical Teachers to Build Students’ Clinical Understanding and Competency
Keywords:
generative AI, clinicaleducation, competency, assessment, clinicalreasoning, ethics, digitallearningAbstract
Background: Generative AI and large language models (LLMs) are increasingly used in medical education to support clinical reasoning, feedback, and competency-based training. Their rapid adoption requires structured educational guidance. Objective: To outline a practical framework for integrating AI into clinical education while maintaining validity, ethics, and patient safety. Methods: A focused narrative review (2023–2026) examined literature on AI in medical education, competency-based frameworks, and assessment theory. Results: LLMs enhance case-based learning, simulated clinical reasoning, OSCE preparation, and structured feedback. When aligned with programmatic assessment and faculty oversight, AI can strengthen higher-order cognitive skills. Key risks include hallucination, bias, and overreliance, requiring verification training and clear policy boundaries. Conclusion: AI should act as a cognitive support tool—not a replacement for clinical judgment. Thoughtful integration within competency-based systems can improve clinical understanding while preserving academic rigor.
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