Digital Health Technologies in Professional Healthcare Training: A Conceptual Framework for AI-Based Adaptive Clinical Simulation and Health Communication

Digital health technologies are reshaping professional healthcare training by enabling more adaptable approaches to clinical simulation, health communication practice, learner assessment, and adaptive learning. However, existing approaches often address these skills separately, with limited integration of clinical reasoning, competence-focused assessment, dynamic simulated-patient responses, patient-centered communication, and adaptive feedback within a single learning cycle. This article presents an evidence-informed conceptual framework for AI-based adaptive clinical simulation and health communication in healthcare training. A structured literature review and conceptual synthesis were used to identify educational requirements and design implications across six domains: clinical reasoning, generative artificial intelligence, simulation and virtual patients, patient-centered communication and empathy, competency-oriented assessment and adaptive learning, and safety and governance. The proposed framework incorporates seven design requirements within an 11-stage closed loop linking learner interaction, behavior capture, simulated-patient state updating, assessment, adaptive response, performance analytics, feedback, and scenario progression. The simulated patient is represented through interconnected clinical, informational, affective, and interactional states, allowing learner behavior to influence subsequent information disclosure and interaction within predefined educational and safety boundaries. Competency standards, clinical guardrails, escalation rules, and high-stakes decisions remain under educator and institutional governance. The framework is proposed as a conceptual design model rather than a validated educational intervention. Its value therefore requires the staged evaluation of technical consistency, assessment validity, safety, educational effectiveness, retention, and behavioral transfer before claims regarding educational or clinical outcomes can be established.