{
  "abstract": "Large language models (LLMs) are increasingly applied to clinical decision support for traditional Chinese medicine (TCM). However, current evaluation practices remain inadequate. Uncurated knowledge sources, such as un-selected ancient texts and web-crawled data, may contain outdated or prohibited recommendations. Specific clinical cases, even a single case, used as the benchmark may mislead the evaluation by individual experience instead of evidence-based consensus. Coarse-grained scoring strategies, such as true/false selections or holistic Likert ratings, obscure component-level errors and underestimate clinical complexity. TCM clinical reasoning is multidimensional, encompassing multiple coexisting syndromes and detailed procedural parameters. We propose a path forward built on two pillars: (1) structuring authoritative knowledge, including national clinical guidelines, industry standards, standard textbooks, the Chinese Pharmacopoeia, and consensus derived from large-scale high-quality cases; and (2) fine-grained, component-wise evaluation, in which complex TCM reasoning is decomposed into minimal assessable units across diagnosis and treatment, scored with component-level precision/recall and error grading, and judged primarily by the identification of critical errors that could cause medical harm, supplemented by expert-assessed global acceptability. Such quality-grounded, component-wise frameworks would enhance scientific credibility, support regulatory scrutiny and clinical adoption, and enable targeted model improvement aligned with the WHO Traditional Medicine Strategy 2025-2034.",
  "abstract_shown": true,
  "affiliations": [
    "WangQi Academy, National Institute of Traditional Chinese Medicine Constitution and Preventive Treatment of Diseases, Beijing University of Chinese Medicine, Beijing, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Huaiyu Wang",
      "orcid": "0000-0002-9093-8793"
    },
    {
      "affs": [
        0
      ],
      "name": "Ji Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Qi Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/98krz5q1yh9n5.bib",
  "cited_by": 0,
  "doi": "10.1515/mr-2026-0006",
  "fulltext_url": "https://europepmc.org/articles/PMC13690056",
  "id": "98krz5q1yh9n5",
  "issns": [
    "2097-0773",
    "2749-9642"
  ],
  "landing_url": "https://europepmc.org/article/MED/42923809",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7212017318",
  "pdf_url": "https://europepmc.org/articles/PMC13690056?pdf=render",
  "pmcid": "PMC13690056",
  "pmid": "42923809",
  "published": "2026-09-01",
  "publisher": "De Gruyter",
  "ris": "https://research.draconitic.com/paper/98krz5q1yh9n5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:02Z",
      "native_id": "MED:42923809",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42923809"
    },
    {
      "first_seen": "2026-10-30T13:47:20Z",
      "native_id": "7ca79adb00ee4031926edfbe30751133",
      "source": "doaj",
      "url": "https://doi.org/10.1515/mr-2026-0006"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Rethinking large language model evaluation in traditional Chinese medicine: toward quality-grounded standards and fine-grained assessment.",
  "title_text": "Rethinking large language model evaluation in traditional Chinese medicine: toward quality-grounded standards and fine-grained assessment.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:47:20Z",
  "url": "https://research.draconitic.com/paper/98krz5q1yh9n5",
  "venue": "Medical review (2021)",
  "versions": []
}