{
  "abstract": "Objective: The objective of this study was to evaluate the diagnostic performance of GPT-5.2 Thinking in identifying oral potentially malignant disorders within a patient sample that also included oral carcinomas and other oral cavity lesions, using intraoral photographs and associated clinical data. Methods: This retrospective exploratory diagnostic accuracy study included 103 oral lesions: 69 oral potentially malignant disorders, 25 oral carcinomas, and 9 other oral pathologies. Each case was assessed in three independent sessions, and the majority diagnosis was compared with the final clinical and/or histopathological reference diagnosis. Results: For oral potentially malignant disorders, sensitivity was 92.75%, specificity was 88.24%, accuracy was 91.26%, positive predictive value was 94.12%, negative predictive value was 85.71%, and Cohen's kappa was 0.804. Sensitivity was 81.82% for homogeneous leukoplakia, 78.57% for non-homogeneous leukoplakia, and 86.36% for oral lichen planus. For oral carcinomas, sensitivity was 84.00%, specificity was 97.44%, accuracy was 94.17%, positive predictive value was 91.30%, negative predictive value was 95.00%, and Cohen's kappa was 0.837. Conclusions: GPT-5.2 Thinking showed promising diagnostic performance in this exploratory case-enriched dataset, but predictive values require validation in representative clinical populations.",
  "abstract_shown": true,
  "affiliations": [
    "Interdisciplinary Department of Medicine, University of Bari \"Aldo Moro\", 70124 Bari, Italy.",
    "Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania \"Luigi Vanvitelli\", 80138 Naples, Italy."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Domenico De Falco",
      "orcid": "0000-0002-3487-0800"
    },
    {
      "affs": [
        0
      ],
      "name": "Fabiana Pinto"
    },
    {
      "affs": [
        1
      ],
      "name": "Alberta Lucchese",
      "orcid": "0000-0002-2677-0869"
    },
    {
      "affs": [
        0
      ],
      "name": "Massimo Petruzzi",
      "orcid": "0000-0002-9030-3739"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/et0pme9301knq.bib",
  "cited_by": 0,
  "doi": "10.3390/dj14100657",
  "fulltext_url": "https://europepmc.org/articles/PMC13703694",
  "id": "et0pme9301knq",
  "issns": [
    "2304-6767"
  ],
  "landing_url": "https://europepmc.org/article/MED/42930423",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3506|Oral Health Pathology and Treatment"
    ]
  ],
  "openalex": "W7221338351",
  "pdf_url": "https://europepmc.org/articles/PMC13703694?pdf=render",
  "pmcid": "PMC13703694",
  "pmid": "42930423",
  "published": "2026-10-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/et0pme9301knq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "MED:42930423",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42930423"
    }
  ],
  "subjects": [],
  "title": "Artificial Intelligence-Based Evaluation of Oral Potentially Malignant Disorders Using Photographic and Clinical Data: A Retrospective Exploratory Diagnostic Accuracy Study.",
  "title_text": "Artificial Intelligence-Based Evaluation of Oral Potentially Malignant Disorders Using Photographic and Clinical Data: A Retrospective Exploratory Diagnostic Accuracy Study.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/et0pme9301knq",
  "venue": "Dentistry journal",
  "versions": []
}