Background: Artificial intelligence (AI) is rapidly reshaping medical education by introducing novel pedagogical tools, adaptive learning environments, and data-driven assessment systems. While the technological momentum is evident, the educational, ethical, and regulatory implications of AI integration require systematic scholarly synthesis grounded in empirical evidence.

Objective: This study aims to develop a comprehensive, theory-driven, and evidence-based analysis of AI applications in medical education, synthesizing findings from recent systematic reviews, scoping reviews, randomized controlled trials, simulation studies, and ethical-legal scholarship.

Methods: A narrative-integrative research methodology was employed, strictly based on peer-reviewed literature published between 2018 and 2025. The analysis synthesizes evidence across multiple educational domains, including undergraduate, postgraduate, and continuing medical education; simulation-based learning; assessment and feedback; clinical reasoning development; and professional skill acquisition. Ethical, legal, and governance considerations were examined through established frameworks, particularly data protection and human oversight mandates.

Results: The literature demonstrates that AI-enhanced educational interventions improve learner engagement, diagnostic reasoning, procedural competence, and personalized feedback mechanisms. Large language models, virtual patients, adaptive simulation, and machine learning-based assessment systems consistently outperform or complement traditional educational methods across multiple disciplines. However, variability in study quality, lack of long-term outcome data, and uneven faculty readiness remain significant constraints. Ethical challenges related to data privacy, algorithmic bias, transparency, and learner autonomy are recurrent themes.

Conclusion: AI represents a structural transformation of medical education rather than a supplementary innovation. Its successful integration requires pedagogical alignment, robust ethical governance, interdisciplinary faculty development, and continuous empirical validation. Future educational architectures must balance technological potential with professional values, human judgment, and regulatory compliance to ensure equitable and sustainable advancement of medical education.

Artificial intelligence, medical education, simulation-based learning

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