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. Public interest summary: Cardiovascular rehabilitation helps people recover after a heart event, but many patients face barriers to attending in-person programs, particularly due to distance, mobility, or limited service availability. SITeCard is a digital platform developed to support remote and hybrid cardiovascular rehabilitation by organizing patient data and providing clinicians with explainable AI-based risk assessments to inform multidisciplinary discussions. The system was co-designed with healthcare teams to ensure usability and clinically meaningful explanations, and it can be accessed through standard smartphones, including in low-connectivity settings. This study reports early, user-centered evaluation results and highlights policy and governance con-siderations relevant to the adoption of explainable AI tools in rehabilitation services.