Health-professions education has moved beyond traditional lecture-based instruction toward competency-based and student-centred learning approaches, such as Problem-Based and Case-Based Learning. While pedagogy has evolved, the structured integration of artificial intelligence (AI) into medical and health-professions education remains limited. Current curricula provide minimal exposure to AI fundamentals, data literacy, and ethics, leaving future clinicians underprepared for AI-enabled practice. This paper extends the Learning, Cognition, AI, and Pedagogy (L-CAP) framework, originally introduced in a continuing-professional-development programme for educators, to health-professions education. Grounded in cognitive science, pedagogy, and software-architecture principles, L-CAP provides a structured, human-centred model for embedding AI through four interdependent layers: Learning, Cognition, AI, and Pedagogy, linked by a Plan-Orchestrate-Assess-Reflect workflow. The framework supports applications in clinical reasoning, inter-professional learning, and ethics education. Early exploratory feedback from the United Kingdom and South Korea suggests that L-CAP is clear, adaptable, and suitable for integration across medical and allied-health curricula. L-CAP thus offers a practical bridge between pedagogy and technology, supporting more integrated, AI-informed, and cognitively grounded health-professions education.