ArticleScientific data2025
TCMEval-SDT: a benchmark dataset for syndrome differentiation thought of traditional Chinese medicine.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Large language models linking traditional Chinese medicine knowledge and clinical practice.Chinese medicine · 2026Review
- Beyond transparency: why Traditional Chinese Medicine (TCM) need explainable artificial intelligence (XAI).Chinese medicine · 2026Review
- Traditional Chinese Medicine Modernization in Diagnosis and Treatment: Utilizing Artificial Intelligence and Nanotechnology.MedComm · 2026Review
- A roadmap for medical large language models: a review of foundations, applications, and challenges.Military Medical Research · 2026Review
- Intelligent data governance and quality control for chest Bi syndrome/coronary heart disease across the prevention-treatment-rehabilitation continuum: integrating a standardized framework, adversarially-optimized trigger engine, and domain-adaptive AI.Frontiers in medical technology · 2026Article
- Efficacy and safety of sini powder combined with Xiaoxianxiong decoction for adolescent depression with liver qi stagnation and phlegm-heat obstruction syndrome: a randomized controlled trial.Frontiers in pediatrics · 2026Article
- TCMEval-PA: a question-answering benchmark dataset for the prescription audit of Traditional Chinese Medicine.Scientific data · 2025Article
- Deep learning approach for objective differentiation of kidney deficiency syndrome in reproductive age females: a tongue-face fusion model.Frontiers in physiology · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
This paper presents a large publicly available benchmark dataset (TCMEval-SDT) for the thought process involved in syndrome differentiation in traditional Chinese medicine (TCM). The dataset consists of 300 TCM syndrome diagnosis cases sourced from the internet, classical Chinese medical texts, and medical records from hospitals, with metadata adhering to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. Each case has been annotated and curated by TCM experts and includes medical record ID, clinical data, explanatory summary, TCM syndrome, clinical information, and TCM pathogenesis, to support algorithms or models in emulating the diagnostic process of TCM clinicians. To provide a comprehensive description of the TCM syndrome diagnosis process, we summarize the diagnosis into four steps: (1) clinical information extraction, (2) TCM pathogenesis reasoning, (3) TCM syndrome reasoning, and (4) explanatory summary. We have also established validation criteria to evaluate their ability in TCM clinical diagnosis using this dataset. To facilitate research and evaluation in syndrome diagnosis of TCM, the TCMEval-SDT dataset is made publicly available under the CC-BY 4.0 license.
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