Semantic distance metrics in anatomy education: Toward AI-assisted mapping of anatomy-to-clinical reasoning.

Anatomy education traditionally emphasizes structural knowledge acquisition, with the implicit assumption that clinical reasoning capabilities will naturally follow. However, the cognitive pathway from anatomical understanding to clinical application is rarely linear or uniform. This article proposes a conceptual framework in which semantic distance-the conceptual separation between anatomical knowledge and clinical endpoints-operates at three analytically distinct levels: (1) the learner's internal cognitive network, shaped by prior experience and schema formation; (2) the structural distance within curriculum maps, ontologies, or knowledge graphs; and (3) the distributional proximity within large language model (LLM) embedding spaces, which may serve as a scalable computational proxy indicator for the first two levels. These three levels must be explicitly distinguished if semantic distance is to function as a useful construct in anatomy education. To illustrate the framework's utility, three worked examples are developed-the femoral nerve, coronary circulation, and spinal cord anatomy-accompanied by a preliminary computational illustration of embedding distances rather than an empirically validated diagnostic instrument. A practical educator workflow is proposed through which computational signals may be translated into targeted curriculum decisions. The article positions semantic distance metrics as complementary to curriculum network analytics and highlights how cross-domain transitions (from anatomy through physiology and pathology to clinical presentation) represent qualitatively different educational problems requiring different interventions. Prospective validation studies, multimodal computational approaches, and longitudinal curriculum outcome analyses are needed before this framework can be applied systematically.