{
  "abstract": "This study compared the educational suitability, overall quality, translation-based readability, clinical intent alignment, and safety of frozen shoulder patient education materials generated by GPT-5, DeepSeek, Doubao, Wenxin Yiyan, and Tongyi Qianwen. Twenty clinician-curated questions covering five content categories were submitted once to each model, producing 100 first-pass responses. C-PEMAT and GQS assessed educational quality, seven English readability formulas were applied to consensus translations of the Chinese outputs, and two preliminary clinician-developed measures assessed clinical key-point coverage and safety-related caution. C-PEMAT and GQS differed significantly among models (both P < 0.001), with GPT-5 obtaining the highest scores, followed by DeepSeek and Doubao. Quality scores did not differ significantly across content categories, whereas all readability indices did. C-PEMAT and GQS were moderately correlated ( r = 0.68), but quality measures were weakly associated with most readability indices. GPT-5 and DeepSeek showed relatively favorable Clinical Intent Alignment scores, while GPT-5 had the highest descriptive Clinical Safety Score. These findings are specific to the tested access date, platforms, prompts, and model versions. LLM-generated materials may serve as auxiliary drafts, but clinician review remains necessary for medical accuracy, safety, readability, and patient-specific appropriateness.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Jiachen Liang"
    },
    {
      "name": "Wanying Gui"
    },
    {
      "name": "Huanan Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e6g2hzcnqwan9.bib",
  "cited_by": 1,
  "doi": "10.1038/s41598-026-66627-6",
  "fulltext_url": null,
  "id": "e6g2hzcnqwan9",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-66627-6",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7220430727",
  "pdf_url": "https://www.nature.com/articles/s41598-026-66627-6.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/e6g2hzcnqwan9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.1038/s41598-026-66627-6",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-66627-6"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Performance of five large language models in frozen shoulder patient education: a multidimensional evaluation of readability, quality, clinical alignment, and safety",
  "title_text": "Performance of five large language models in frozen shoulder patient education: a multidimensional evaluation of readability, quality, clinical alignment, and safety",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/e6g2hzcnqwan9",
  "venue": "Scientific Reports",
  "versions": []
}