Artificial Intelligence in Anatomic Education: Educational Utility, Safety Boundaries, and Implementation Considerations

Anatomic education is central to medical training and underpins safe clinical and surgical practice. Despite its importance, traditional methods of anatomy teaching face persistent structural challenges, including limited cadaver availability, high costs, logistical constraints, and restricted ability to demonstrate dynamic and patient-specific anatomic relationships. Artificial intelligence (AI) has emerged as a collection of computational tools that may support anatomic education by enhancing visualization, enabling structured repetition, and expanding access to educational resources. This article provides a critical synthesis of contemporary AI applications relevant to anatomic education, focusing on computer vision, deep learning-based visualisation, learning analytics, and natural language processing. Emphasis is placed on educational utility rather than clinical automation, with attention to validation of anatomic accuracy, risks of misinformation and hallucinations in generative systems, algorithmic bias, cost and infrastructure requirements, and professional accountability. Current evidence suggests that AI-supported tools can complement anatomy-led curricula when implemented with appropriate safeguards, human oversight, and governance. Careful integration is required to ensure that AI augments, rather than compromises, the foundational standards of anatomic education and trainee preparedness for imaging interpretation and procedural planning.