Abstract

Objectives To report the development and early formative, user-centered evaluation of a human-centric explainable artificial intelligence (AI)-enabled platform for remote and hybrid phase II cardiovascular rehabilitation (CR), and to discuss its policy and technology implications for adoption and governance in health systems facing access constraints. Methods A four-stage methodology was applied: (1) multidisciplinary needs elicitation with cardiovascular rehabilitation professionals; (2) development of machine-learning models for rehabilitation-related risk assessment with integrated explainability; (3) adaptation of expla-nations to clinicians’ and patients’ mental models; and (4) system implementation followed by early multidisciplinary evaluation focused on usability, perceived clinical utility, and safety positioning as a second-opinion decision support tool. Results The platform integrates remote patient monitoring, explainable risk assessment, and coordinated multidisciplinary workflows. In early formative evaluation, healthcare professionals reported high acceptance of the explainable second-opinion functionality, highlighting improved interpretability and support for rehabilitation assessment and discharge-related discussions, without replacing clinical judgment. Conclusions This study provides an early-stage, policy-relevant account of how explainable AI can be operationalized in cardiovascular rehabilitation while remaining aligned with clinical practice and governance expectations. Rather than demonstrating system-level impact, the con-tribution lies in outlining a practical framework for evaluating adoption conditions, governance needs, and future scale-up of AI-enabled rehabilitation technologies.