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Risk Management of Large Language Model-Based Exercise and Health Guidance: A China-Anchored; Comparatively Informed Six-Dimensional Trigger Matrix and Lifecycle Governance Framework for the Wellness-to-SaMD Continuum
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DOI：10.2147/rmhp.s624615.png)
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Abstract
En 中文
LLM-based exercise and health guidance creates a distinctive risk-management challenge: seemingly modest changes in product claims, target users, data inputs, personalization, automation, human oversight, or updates may move a tool from general wellness support toward higher-risk medical use. In exercise prescription and rehabilitation, an unsafe recommendation can affect physical load, recognition of warning symptoms, and timely referral. We conducted a structured narrative review and doctrinal/comparative legal analysis, anchored in China’s National Medical Products Administration (NMPA) framework and informed by the European Union Medical Device Regulation (MDR), the EU Artificial Intelligence Act, and US Food and Drug Administration (FDA) and International Medical Device Regulators Forum (IMDRF) materials. Peer-reviewed literature was primarily searched for 2019– 2026, with foundational regulatory, legal, and technical guidance included where directly relevant. We propose a six-dimensional trigger matrix covering intended use and claims, user context, depth of personalization, data and sensor sources, automation, human oversight and closed-loop control, and upgrade and change pathways. The framework includes anchored Green/Yellow/Red coding rules, non-compensatory aggregation rules, a structured governance checklist, and a lifecycle pathway for evidence generation, risk management, and change control. In a preliminary application exercise, three independent raters applied the coding rules to seven standardized hypothetical scenarios and achieved complete agreement on all dimension-level and overall designations (Fleiss’ kappa = 1.00). This small exercise supports initial reproducibility of the rubric but does not establish legal classification accuracy, clinical validity, or real-world effectiveness. The proposed framework is intended to support earlier risk identification, evidence planning, procurement review, and dialogue among developers, healthcare institutions, and regulators; it does not replace product-specific legal analysis or regulatory determination.
Keywords:
healthcare risk management
artificial intelligence
large language models
software as a medical device
exercise prescription
human oversight
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