{
  "abstract": "Background/Objectives: To evaluate the concordance of three general-purpose large language models (LLMs), ChatGPT, Claude, and Gemini, with an adjudicated expert consensus classification of Stage, Grade, and Extent in Stage III–IV periodontitis when applying the 2018 American Academy of Periodontology/European Federation of Periodontology (AAP/EFP) rules to clinician-prepared, standardized case summaries. Methods: In this multicenter retrospective concordance study (two centers, 105 patients), two periodontologists independently classified each case from complete charts and radiographs; discordant cases were adjudicated. De-identified summaries containing pre-extracted clinical, radiographic, and modifier variables were presented to each LLM in three independent sessions per case; the modal decision was the primary index result, and single-session performance was also reported. Stage and Grade concordance (Cohen’s/weighted kappa) were co-primary endpoints; Extent and full concordance were secondary. Results: Modal Stage concordance was almost perfect for ChatGPT (κ = 0.98) and Gemini (κ = 0.92) and substantial for Claude (κ = 0.73); single-session Stage κ ranged from 0.73 to 0.98. Grade (weighted κ = 0.97–1.00) and Extent (κ = 0.93–1.00) concordance were almost perfect. Full concordance ranged from 85.7% (Claude) to 99.0% (ChatGPT). Every classifiable modal Stage discordance understated severity: Claude and Gemini assigned Stage II or I to 14 Stage III cases in which attachment loss met the Stage III threshold but radiographic bone loss was below 33%. Conclusions: Given pre-extracted variables and explicit instructions, the LLMs applied the classification rules with substantial to almost perfect concordance; this reflects rule application, not independent diagnosis. Performance was model-specific, Gemini showed greater session-to-session variability, and two models systematically understaged borderline Stage III cases. The findings support only clinician-supervised, educational use with local validation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Periodontology, Faculty of Dentistry, Batman University, 72060 Batman, Türkiye",
    "Department of Periodontology, Faculty of Dentistry, Burdur Mehmet Akif Ersoy University, 15030 Burdur, Türkiye",
    "Department of Periodontology, Faculty of Dentistry, Alanya Alaaddin Keykubat University, 07410 Alanya, Türkiye"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mehmet Kızıltoprak",
      "orcid": "0000-0001-5829-4812"
    },
    {
      "affs": [
        1
      ],
      "name": "Mustafa Karaca",
      "orcid": "0000-0001-5853-2366"
    },
    {
      "affs": [
        2
      ],
      "name": "Mustafa Özay Uslu",
      "orcid": "0000-0002-9707-1379"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/78076w1vj8tcr.bib",
  "cited_by": 0,
  "doi": "10.3390/jcm15197585",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13665847/?tool=EBI",
  "id": "78076w1vj8tcr",
  "issns": [
    "2077-0383"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13665847",
  "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",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7214948205",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13665847/pdf/?tool=EBI",
  "pmcid": "PMC13665847",
  "pmid": null,
  "published": "2026-09-30",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/78076w1vj8tcr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:35Z",
      "native_id": "PMC:PMC13665847",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13665847"
    },
    {
      "first_seen": "2026-10-30T12:20:00Z",
      "native_id": "10.3390/jcm15197585",
      "source": "crossref",
      "url": "https://www.mdpi.com/2077-0383/15/19/7585"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Staging and Grading Advanced Periodontitis with Large Language Models from Standardized Case Summaries: A Multicenter Concordance and Reproducibility Study",
  "title_text": "Staging and Grading Advanced Periodontitis with Large Language Models from Standardized Case Summaries: A Multicenter Concordance and Reproducibility Study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:00Z",
  "url": "https://research.draconitic.com/paper/78076w1vj8tcr",
  "venue": "Journal of clinical medicine",
  "versions": []
}