ArticleNPJ digital medicine2025
Evaluating the role of large language models in traditional Chinese medicine diagnosis and treatment recommendations.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Design and methodology of the AI-empowered Clinical Evidence for Integrated Chinese-Western Medicine (ACE-iMed) platform.Integrative medicine research · 2026Article
- Exploring large language models as a prescription decision support tool for rational antibiotic use: A dual-framework analysis using standardized examinations and real-world clinical cases.Exploratory research in clinical and social pharmacy · 2026Article
- Medical large language models and systems in the clinical application of spinal diseases: Current status, challenges, and future prospects.Journal of orthopaedic translation · 2026Review
- Decoding AI Competence: Benchmarking Large Language Models (LLMs) in Ovarian Cancer Diagnosis and Treatment-A Systematic Evaluation of Generative AI Accuracy and Completeness.Diagnostics (Basel, Switzerland) · 2026Article
- Preferences of Chinese Dermatologists for Large Language Model Responses in Clinical Psoriasis Scenarios: A Nationwide Cross-Sectional Survey in China.Health care science · 2026Article
- Are AI tutors ready for the TCM classroom? A multimodal evaluation of large language models across cognitive levels in Chinese medicine education.Frontiers in medicine · 2026Article
- A comparative study on the credibility of ischemic stroke treatment information on social media platforms: evidence from Weibo and REDnote.Frontiers in digital health · 2026Article
- Risk-centered benchmarking of large language models for AI-enabled counseling in chronic autoimmune thyroid eye disease.Frontiers in cell and developmental biology · 2026Article
- Comparative performance of large language models for patient-initiated ophthalmology consultations.Frontiers in public health · 2025Article
- Performance of GPT-4 for planning acupuncture treatment: comparison with human clinician performance.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital health technologies hold significant potential for reducing global healthcare disparities. Large language models (LLMs) offer new opportunities to enhance access to culturally specific healthcare, including traditional Chinese medicine (TCM). This study evaluated the diagnostic and treatment performance of seven publicly available LLMs using a real-world acupuncture case, comparing their outputs with three professional acupuncturists across five domains: Western diagnosis, TCM diagnosis, acupoint selection, needling technique, and herbal medicine. Twenty-eight expert evaluators from China, South Korea, and the United States assessed the responses using a multilingual survey. LLMs performed comparably to acupuncturists in Western diagnosis and showed variable performance in TCM-specific tasks. GPT-4o, Qwen 2.5 Max, and Doubao 1.5 Pro demonstrated the highest alignment with expert evaluations, particularly in TCM diagnosis and acupoint selection. These findings highlight the potential of general-purpose LLMs to support culturally grounded medical decision-making and reduce access barriers in TCM care systems.
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Registered trials
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 OpenQuestion graph.