ReviewRisk management and healthcare policy2026
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.
Review in Risk management and healthcare policy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.