ARTIFICIAL INTELLIGENCE IN MEDICAL TRAINING: A FRAMEWORK FOR MANAGING HALLUCINATIONS, BIAS, AND STUDENT OVER-RELIANCE
Keywords:
Artificial intelligence, medical education, large language models, clinical reasoning, automation bias, hallucinations, AI governance, medical training.Abstract
The rapid development of large language models (LLMs) has transformed the landscape of medical education. AI-powered tutoring systems are increasingly used by medical students for concept explanation, self-assessment, examination preparation, and clinical case discussions. While these technologies offer substantial educational benefits, their integration into medical training raises concerns regarding hallucinations, fabricated references, automation bias, algorithmic bias, and excessive dependence on AI-generated recommendations. This review examines current evidence regarding the responsible implementation of LLM-based educational tools in clinical reasoning training. Literature published between 2011 and 2024 was reviewed, with emphasis on studies conducted after the emergence of advanced generative AI systems. Available evidence suggests that AI tutors can enhance learning efficiency, accessibility, and personalized education. However, documented limitations include factual inaccuracies, reference fabrication, propagation of cognitive biases, and reduction of independent analytical thinking. The review proposes a framework for responsible integration based on transparency, human oversight, evidence verification, active learning strategies, and institutional governance. The future role of AI tutors in medicine should focus on augmenting rather than replacing human teaching and clinical reasoning.
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