Ensuring Safety in Clinical AI: Formally Verified Deep Learning for Heart Failure Detection

A major obstacle in fields including sports, clinical rehabilitation, and workplace safety is the timely detection and prevention of physical injuries. The majority of conventional monitoring systems are reactive, depending on post-event analysis or unimodal data sources, which restricts their ability to provide proactive actions and early warnings. Furthermore, current AI-driven health systems lack rigorous validation procedures, which compromises their suitability for practical implementation in safety-critical settings. In this work, we present MHIDS (Multimodal Hybrid Injury Detection System), an integrated, AI-based diagnostic framework that combines wearable physiological sensors, computer vision, and personalized physiological modeling for real-time injury forecasting. A continuously updated digital twin is employed to capture each user’s biomechanical and physiological profile, allowing adaptive, individualized risk assessment. Unlike conventional approaches, MHIDS incorporates a closed-loop feedback mechanism that dynamically reconfigures sensing parameters and provides actionable recommendations (e.g., posture correction, intensity adjustment, or rest scheduling), thereby shifting the paradigm from passive detection to proactive prevention. To guarantee correctness and operational trustworthiness, MHIDS is formally modeled in UPPAAL as a network of timed automata, ensuring critical properties such as bounded response times (<100 ms), safety, liveness, and deadlock freedom. Experimental validation using the publicly available MHEALTH dataset demonstrates superior predictive performance, achieving an accuracy of 99.21%, precision of 98.94%, recall of 99.07%, and F1-score of 99.00%, significantly outperforming state-of-the-art baselines.