Luo, Xuexing, Li, Yiyuan, Xu, Jing et al. · Journal of medical Internet research · 2025 · DOI
Researchers reviewed 14 studies about using artificial intelligence (AI) to improve medical questionnaires—the forms doctors use to diagnose conditions. They found that AI can help doctors distinguish ME/CFS from long COVID with over 92% accuracy, develop better questionnaires, and predict disease risk. However, most research is still early-stage, and more testing is needed before AI tools are ready for everyday clinical use.
For ME/CFS patients and researchers, this review highlights AI's potential to improve diagnostic accuracy and differentiate ME/CFS from similar conditions like long COVID—a critical challenge since misdiagnosis delays appropriate treatment. The finding that AI can assess questionnaires with high accuracy suggests promise for developing better diagnostic tools specific to ME/CFS, though the field remains largely exploratory.
This review does not prove that AI questionnaire tools are ready for clinical use—only 21% of identified studies have reached clinical validation. The review also does not establish how well these AI approaches would work specifically for ME/CFS diagnosis in real-world clinical settings, nor does it demonstrate that improved questionnaire assessment directly translates to better patient outcomes.
About the PEM badge: “PEM required” means post-exertional malaise was an explicit required diagnostic criterion for participant inclusion in this study — not that PEM was studied, observed, or discussed. Studies using criteria that do not require PEM (e.g. Fukuda, Oxford) are tagged “PEM not required”. How the atlas works →
The first block is for the primary paper and is the citation you should use in research work. The atlas-snapshot line only applies if you are specifically referring to this atlas’s reading of the paper on the date shown.
Primary citation
Luo, Xuexing, Li, Yiyuan, Xu, Jing, Zheng, Zhong, Ying, Fangtian, & Huang, Guanghui (2025). AI in Medical Questionnaires: Scoping Review.. Journal of medical Internet research. https://doi.org/10.2196/72398
BibTeX
@article{mecfsatlas-luo-2025-medical-questionnaires,
author = {Luo, Xuexing and Li, Yiyuan and Xu, Jing and Zheng, Zhong and Ying, Fangtian and Huang, Guanghui},
title = {AI in Medical Questionnaires: Scoping Review.},
journal = {Journal of medical Internet research},
year = {2025},
doi = {10.2196/72398},
note = {PubMed: 40549427},
url = {https://www.mecfsatlas.com/evidence/luo-2025-medical-questionnaires},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-27. https://www.mecfsatlas.com/evidence/luo-2025-medical-questionnaires
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