Evidence mapPaperPMID 42524332Full record

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.

Kaijiang Pan, Xinyu Lin, Shengqi Huang, Caihua Huang

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kaijiang PanSchool of Marxism, Xiamen Ocean Vocational College, Xiamen, Fujian, People's Republic of China.ORCID 0009-0006-0108-1035
Xinyu LinSchool of Film and Communication, Xiamen University of Technology, Xiamen, Fujian, People's Republic of China.ORCID 0009-0008-8182-2431
Shengqi HuangHuandaolu (Xiamen) Sports and Health Service Co., Ltd, Xiamen, Fujian, People's Republic of China.ORCID 0009-0008-0522-478X
Caihua HuangResearch and Communication Center for Exercise and Health, Xiamen University of Technology, Xiamen, Fujian, People's Republic of China.ORCID 0000-0001-5134-0169

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligenceexercise prescriptionhealthcare risk managementhuman oversightlarge language modelssoftware as a medical device

Identifiers

PMID42524332
PMCPMC13411149

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.