{
  "abstract": "Introduction: Large language models (LLMs) are increasingly evaluated for diagnostic applications; however, studies in oral and maxillofacial pathology (OMFP) assess materially different diagnostic tasks and outcomes. This systematic review synthesized evidence according to the specific task evaluated.\n\nMethods: PubMed, Web of Science, and Cochrane Central were searched without language or date restrictions, supplemented by gray-literature screening and citation tracking. Studies evaluating defined OMFP diagnostic tasks using task-appropriate outcomes were included. Screening, data extraction, and quality assessment were independently performed by 2 reviewers. Owing to clinical and methodological heterogeneity, evidence was synthesized narratively by diagnostic task.\n\nResults: Sixteen studies published between 2024 and 2025 were included. Seven evaluated single-diagnosis identification, 6 differential-diagnosis generation or ranking, 5 binary or multiclass classification/detection, and 1 structured diagnostic report generation; counts were non-mutually exclusive. Exact-diagnosis performance varied considerably and did not demonstrate consistent specialist equivalence. Differential-diagnosis studies generally reported higher Top-k inclusion than Top-1 accuracy, but these outcomes did not establish exact diagnostic correctness. Classification studies frequently reported high specificity but variable sensitivity. The single report-generation study reported item-level rather than whole-case diagnostic outcomes.\n\nConclusion: Large language models may support selected OMFP tasks, particularly differential-diagnosis generation. However, exact-diagnosis performance remains inconsistent, classification sensitivity is task-dependent, and evidence for structured report generation is limited. Current evidence does not support a single overall estimate of diagnostic performance or autonomous diagnostic use.",
  "abstract_shown": true,
  "affiliations": [
    "King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia",
    "Department of Physiology, Azra Naheed Dental College, Superior University, Lahore, Pakistan",
    "King Abdullah International Medical Research Center, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia",
    "Department of Oral Implantology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China"
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Sohaib Shujaat"
    },
    {
      "affs": [
        1
      ],
      "name": "Marryam Riaz"
    },
    {
      "affs": [
        2
      ],
      "name": "Hisham Albrikeet"
    },
    {
      "affs": [
        3
      ],
      "name": "Hongyang Ma"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/39n90gty2328f.bib",
  "cited_by": 0,
  "doi": "10.1159/pat/aehag008",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13722466/",
  "id": "39n90gty2328f",
  "issns": [
    "1015-2008",
    "1423-0291"
  ],
  "landing_url": "https://doi.org/10.1159/pat/aehag008",
  "language": "en",
  "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": [
    [
      "heading",
      "Systematic Review"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7222674491",
  "pdf_url": "https://karger.com/pat/article-pdf/doi/10.1159/pat/aehag008/4595608/000554920.pdf",
  "pmcid": "PMC13722466",
  "pmid": "42886619",
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/39n90gty2328f.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:22:57Z",
      "native_id": "PMC13722466",
      "source": "pmc",
      "url": "https://doi.org/10.1159/pat/aehag008"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Performance of Large Language Models Across Diagnostic Tasks in Oral and Maxillofacial Pathology: A Systematic Review",
  "title_text": "Performance of Large Language Models Across Diagnostic Tasks in Oral and Maxillofacial Pathology: A Systematic Review",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:22:57Z",
  "url": "https://research.draconitic.com/paper/39n90gty2328f",
  "venue": "Pathobiology",
  "versions": []
}