Artificial Intelligence in Teaching Uterine Fibroids to Medical Students.
Abstract
Artificial intelligence (AI) is increasingly used in medical education, but task-level guidance on when learners should engage with AI during clinical reasoning remains underdeveloped. This Special Article presents a literature-informed conceptual proposal, not an evaluated educational intervention, using uterine fibroids as an illustrative teaching context. A targeted narrative search of PubMed/MEDLINE and the Spanish Journal of Medical Education website identified literature on AI-supported medical education, clinical reasoning, case-based learning, cognitive load, reflection, assessment, and recent AI-integration frameworks and approaches. We propose a five-step supervised sequence: prepare, reason, interact, verify, and reflect. The conceptual contribution is an AI-specific temporal sequencing principle: learners commit to an independent problem representation before AI exposure; AI is then used as a fallible comparator or feedback source; its claims are checked against an external authoritative source; and discrepancies are closed through reflection. The sequence is designed to address AI-specific risks of answer substitution, uncritical acceptance, and closure without reflection while preserving established case-based and feedback-rich learning. It is intended to be transferable to other specialties when tasks require contextual reasoning, source verification, and professional judgement. The framework has not been implemented or empirically evaluated; its feasibility, acceptability, fidelity, workload implications, and effects on learning, clinical reasoning, and competency development require prospective study.
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References
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