{
  "abstract": "Background The remarkable capabilities of large language models make them increasingly compelling for use in real-world healthcare applications. However, the risks associated with using these artificial intelligence systems in medicine are not systematically understood. The aim of this study is to characterize these risks by applying five key principles for safe and trustworthy medical artificial intelligence: truthfulness, resilience, fairness, robustness, and privacy. Methods We introduce MedGuard-Bench: a safety benchmark featuring one thousand expert-verified questions covering ten specific aspects of our five core principles. We use this comprehensive corpus to systematically evaluate sixteen commonly used large language models, assessing their safety and reliability in medical contexts. Results We show that current large language models generally perform poorly on most of our safety tests, regardless of their safety alignment mechanisms. Our evaluation demonstrates that these models fall significantly short when compared to the high performance and reliability of human physicians. Conclusions Despite reports indicating that advanced large language models can match or exceed human performance in various medical tasks, this study reveals a significant safety gap in current technology. This underscores the crucial need for ongoing human oversight and the implementation of strict safety guardrails before deploying these tools in clinical practice.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, University of Maryland at College Park, College Park, MD, USA",
    "Division of Intramural Research, National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA",
    "Department of Computer Science, University of Virginia, Charlottesville, VA, USA",
    "College of Medicine, Howard University, Washington, DC, USA",
    "Jacob Medical Center, Albert Einstein College of Medicine, New York City, NY, USA",
    "School of Medicine, Universidad de Chile, Santiago, Chile",
    "Department of Neurology, University of Florida, Gainesville, FL, USA"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 18,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Yifan Yang",
      "orcid": "0000-0003-4414-9176"
    },
    {
      "affs": [
        1
      ],
      "name": "Qiao Jin",
      "orcid": "0000-0002-1268-7239"
    },
    {
      "affs": [
        1
      ],
      "name": "Robert Leaman",
      "orcid": "0000-0003-3296-5766"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoyu Liu",
      "orcid": "0000-0002-3485-0885"
    },
    {
      "affs": [
        2
      ],
      "name": "Guangzhi Xiong",
      "orcid": "0000-0002-8049-5298"
    },
    {
      "affs": [
        3,
        1
      ],
      "name": "Maame Sarfo-Gyamfi"
    },
    {
      "affs": [
        4
      ],
      "name": "Changlin Gong",
      "orcid": "0000-0001-9932-0538"
    },
    {
      "affs": [
        5
      ],
      "name": "Santiago Ferrière-Steinert",
      "orcid": "0009-0009-8296-4076"
    },
    {
      "affs": [
        1
      ],
      "name": "W. John Wilbur"
    },
    {
      "affs": [
        6
      ],
      "name": "Xiaojun Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiaxin Yuan",
      "orcid": "0000-0003-1739-2381"
    },
    {
      "affs": [
        0
      ],
      "name": "Bang An"
    },
    {
      "affs": [
        5
      ],
      "name": "Kelvin Castro Neira",
      "orcid": "0000-0002-2871-8183"
    },
    {
      "affs": [
        1
      ],
      "name": "Francisco Erramuspe Álvarez"
    },
    {
      "affs": [
        5
      ],
      "name": "Matías Stockle"
    },
    {
      "affs": [
        2
      ],
      "name": "Aidong Zhang",
      "orcid": "0000-0001-9723-3246"
    },
    {
      "affs": [
        0
      ],
      "name": "Furong Huang",
      "orcid": "0000-0002-7393-1047"
    },
    {
      "affs": [
        1
      ],
      "name": "Zhiyong Lu",
      "orcid": "0000-0001-9998-916X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/048m0jyeb0y2k.bib",
  "cited_by": 17,
  "doi": "10.1038/s43856-026-01881-3",
  "fulltext_url": null,
  "id": "048m0jyeb0y2k",
  "issns": [
    "2730-664X"
  ],
  "landing_url": "https://doi.org/10.1038/s43856-026-01881-3",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
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  "pdf_url": "https://www.nature.com/articles/s43856-026-01881-3_reference.pdf",
  "pmcid": "PMC12478422",
  "pmid": "41031074",
  "published": "2026-09-19",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/048m0jyeb0y2k.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T13:20:51Z",
      "native_id": "W7213663248",
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      "url": "https://doi.org/10.1038/s43856-026-01881-3"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Assessing safety and trustworthiness of large language models in medicine",
  "title_text": "Assessing safety and trustworthiness of large language models in medicine",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:16Z",
  "url": "https://research.draconitic.com/paper/048m0jyeb0y2k",
  "venue": "Communications Medicine",
  "versions": []
}