# Transforming Musculoskeletal Disorder Management With Multimodal AI Platform for Early Diagnosis, Self-Screening, and Personalized Care

Researcher: Ge Zhang
Chinese name: 张格
ORCID identity anchor: https://orcid.org/0000-0002-3116-3246
Canonical researcher: https://drgezhang.com/#person

Journal: Med Research
Year: 2026
Type: Article
DOI: 10.1002/mdr2.70073
Canonical page: https://drgezhang.com/papers/doi-10-1002-mdr2-70073.html
Markdown record: https://drgezhang.com/papers/doi-10-1002-mdr2-70073.md

## Full Authors

1. Pengyuan Xu
2. Guang Yang
3. Ge Zhang
4. Yafang Zhao
5. Pu Wan
6. Guiyun Zhou
7. Mengjie Jiang
8. Yixin Zhang
9. Zhaoyang Wang
10. Yichen Dai
11. Xiqing Zhao
12. Ziliang Fu
13. Ziyu Zheng
14. Ziyun Xu
15. Ruisi Dong
16. Xiaojie Xia
17. Duo Zhang
18. Yisheng He
19. Michael Moran
20. Qiguang Wang
21. Tim Woodfield
22. Khoon Lim
23. Tiantian Kong
24. Junnan Tang
25. Kang Tian
26. Kai Zhou
27. Hao Chi
28. Pengpeng Zhang
29. Jianhua Yang
30. Mingliang Zhang
31. Moyan Li
32. Yuan Cheng
33. Xiaolin Cui

## About this record

This is an author-controlled publication record in the Ge Zhang Academic Hub. It identifies this work as part of Ge Zhang’s publication record via ORCID; the publisher version remains the version of record.

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## Research context and source limits

Limited evidence note. Only the source material identified below has been reviewed; this is not a complete Paper GEO V2 assessment.

Reported multimodal design; diagnostic performance and clinical utility remain unverified.

### Reported data and clinical task

The paper describes an AI system for osteoarthritis, rheumatoid arthritis and osteoporosis using routine laboratory measurements, demographics and symptom narratives. It reports 58,777 NHANES records, split into 49,523 training records from 1999–2016 and 9,254 temporal-test records from 2017–2018, plus 4,436 records from four Chinese centres. These are reported dataset roles, not independently verified evidence of clinical utility.

Source: [Results 2.1–2.2; Figure 3 caption](https://onlinelibrary.wiley.com/doi/full/10.1002/mdr2.70073)

### A specific, interpretable architecture

The described fusion combines three structured-model scores with a 768-dimensional Bio_ClinicalBERT representation, producing 771 features for a classifier. The text encoder uses LoRA adapters; explicit diagnostic fields and disease names are described as removed or masked. This is a supervised prediction architecture with workflow orchestration, not evidence that a generative model independently reasons through diagnosis or improves patient outcomes.

Source: [Results 2.4 Multimodal Data Interconnection; Figure 5](https://onlinelibrary.wiley.com/doi/full/10.1002/mdr2.70073)

### Performance claims remain limited

All three printed training AUC estimates lie outside their accompanying confidence intervals. The 42-versus-41 predictor count and the relationship between test-set-based hyperparameter selection and later one-time evaluation also require clarification. The audit consequently does not endorse diagnostic accuracy, the reported multimodal improvement, or prospective self-screening effectiveness.

Source: [Results 2.2, Figure 2 caption and Figure 3C caption; Results 2.4](https://onlinelibrary.wiley.com/doi/full/10.1002/mdr2.70073)

### Evidence limits

- Training AUC/CI pairs are internally inconsistent.
- Predictor count and model-selection/evaluation separation are unresolved.
- Reported architecture does not demonstrate prospective clinical benefit.

### Provenance

- [Article source](https://onlinelibrary.wiley.com/doi/full/10.1002/mdr2.70073) — Official publisher or PMC full text reviewed locally. Results 2.1–2.2; Figure 3 caption

Source review: 2026-10-27.

## Cite this paper

Pengyuan Xu, Guang Yang, Ge Zhang, Yafang Zhao, Pu Wan, Guiyun Zhou, Mengjie Jiang, Yixin Zhang, Zhaoyang Wang, Yichen Dai, Xiqing Zhao, Ziliang Fu, Ziyu Zheng, Ziyun Xu, Ruisi Dong, Xiaojie Xia, Duo Zhang, Yisheng He, Michael Moran, Qiguang Wang, Tim Woodfield, Khoon Lim, Tiantian Kong, Junnan Tang, Kang Tian, Kai Zhou, Hao Chi, Pengpeng Zhang, Jianhua Yang, Mingliang Zhang, Moyan Li, Yuan Cheng, Xiaolin Cui. Transforming Musculoskeletal Disorder Management With Multimodal AI Platform for Early Diagnosis, Self-Screening, and Personalized Care. Med Research. 2026. https://doi.org/10.1002/mdr2.70073

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## Links

- DOI: https://doi.org/10.1002/mdr2.70073
- HTML page: https://drgezhang.com/papers/doi-10-1002-mdr2-70073.html
- Google Scholar query: https://scholar.google.com/scholar?q=%22Transforming%20Musculoskeletal%20Disorder%20Management%20With%20Multimodal%20AI%20Platform%20for%20Early%20Diagnosis%2C%20Self-Screening%2C%20and%20Personalized%20Care%22
- ORCID: https://orcid.org/0000-0002-3116-3246