{
  "abstract": "ObjectivesTo evaluate whether access to a certified large language model (LLM)-based clinical decision support system improves physician diagnostic performance in rheumatology compared with conventional diagnostic resources alone.\n\nMethodsIn this multicentre, open-label, randomised controlled trial, 82 physicians from seven hospitals in two countries were randomised 1:1 to conventional diagnostic resources plus Prof. Valmed or conventional resources alone. Participants assessed three rheumatology vignettes before and after assistance. The primary outcome was top-1 diagnostic accuracy. Secondary outcomes included top-3 accuracy, diagnostic reasoning, confidence, case-processing time and perceived support quality.\n\nResultsTop-1 accuracy increased from 22.2% to 33.3% in the intervention group and from 23.3% to 35.0% in the control group, with no between-group difference in improvement (adjusted OR 0.99, 95% CI 0.45 to 2.19; p=0.979). Differences in top-3 accuracy, diagnostic reasoning and confidence were also not significant. Assisted case-processing time was substantially shorter with LLM support (94 vs 206 s; adjusted mean difference -112 s, 95% CI -141 to -83; p<0.001). Information timeliness and perceived diagnostic support quality were rated significantly higher in the intervention group. Exploratory analyses showed persistent overconfidence and substantial AI over-reliance.\n\nConclusionsCertified LLM-based diagnostic support did not improve diagnostic accuracy compared with conventional resources, but substantially reduced case-processing time and improved perceived support quality. These findings suggest potential workflow benefits while highlighting overconfidence and over-reliance as important safety considerations.\n\nTrial registration numberNCT07166692\n\nWHAT IS ALREADY KNOWN ON THIS TOPICO_LIAccurate and timely diagnosis in rheumatology is challenging because workforce shortages coincide with non-specific, complex and often rare presentations. C_LIO_LILarge language models are increasingly used for diagnostic decision support and have shown strong performance in benchmarking studies. C_LIO_LIRandomised evidence for physician-facing diagnostic decision support, particularly in rheumatology and for certified LLM-based systems, is lacking. C_LI\n\nWHAT THIS STUDY ADDSO_LIThe ALLIANCE trial is the first randomised controlled trial to evaluate physician-facing diagnostic decision support in rheumatology and the first to assess a medically certified LLM-based clinical decision support system in any clinical specialty. C_LIO_LIA certified LLM-based clinical decision support system did not improve top-1 diagnostic accuracy compared with conventional resources alone, as assistance led to similar gains in both groups. C_LIO_LIUse of a certified LLM-based clinical decision support system was associated with approximately half the assisted case-processing time of conventional resources alone, as well as higher perceived support quality. C_LI\n\nHOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICYO_LILLM-based diagnostic decision support may be most useful for improving efficiency, support quality, and broadening differential diagnoses rather than increasing top-1 diagnostic accuracy. C_LIO_LISafe clinical implementation will require careful attention to overconfidence, over-reliance, transparency, and trust. C_LIO_LIFurther robust trials, ideally under real-world clinical conditions, are needed to define the role of clinical decision support in routine care. C_LI",
  "abstract_shown": true,
  "affiliations": [
    "Philipps-Universität Marburg, School of Medicine, Institute for Digital Medicine, Marburg, Germany"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "name": "P. Kremer"
    },
    {
      "name": "N. Schlicker"
    },
    {
      "name": "R. Hasnaj"
    },
    {
      "name": "J. Bamberger"
    },
    {
      "name": "T. Witte"
    },
    {
      "name": "I. Haase"
    },
    {
      "name": "A. Mayr"
    },
    {
      "name": "C. Schmidt"
    },
    {
      "name": "N. Osteras"
    },
    {
      "name": "X. Baraliakos"
    },
    {
      "name": "S. Kuhn"
    },
    {
      "name": "M. Krusche"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Knitza"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e28pjesgvhp8j.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.29.26361715",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.08.29.26361715v1.full",
  "id": "e28pjesgvhp8j",
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  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.08.29.26361715v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "rheumatology"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7206217106",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.08.29.26361715v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e28pjesgvhp8j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:35Z",
      "native_id": "10.64898/2026.08.29.26361715",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.08.29.26361715v1"
    }
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Certified large language model-based diagnostic decision support in rheumatology: the ALLIANCE multicentre randomised controlled trial",
  "title_text": "Certified large language model-based diagnostic decision support in rheumatology: the ALLIANCE multicentre randomised controlled trial",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:35Z",
  "url": "https://research.draconitic.com/paper/e28pjesgvhp8j",
  "venue": "medRxiv",
  "versions": []
}