ArticleOral diseases2025
Diagnostic Performance of Multimodal Large Language Models in the Analysis of Oral Pathology.
Article in Oral diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Periodontitis Risk Assessment and Prevention Planning: Comparative Study of Multimodal Large Language Models and Periodontist Evaluations.Journal of medical Internet research · 2026Article
- Diagnostic Accuracy of Multimodal Large Language Models for Four-Class Benchmark of Oral Autoimmune Blistering Diseases: A Multicenter Paired Study.Diagnostics (Basel, Switzerland) · 2026Article
- Context-Guided Mixture-of-Experts with Multimodal LLMs for Lesion Detection in Buccal Mucosa Images.International dental journal · 2026Article
- Comment on "Diagnostic Performance of Multimodal Large Language Models in the Analysis of Oral Pathology".Oral diseases · 2026Article
- Human versus artificial intelligence in oral pathology diagnosis: a comparative study of ChatGPT, Grok, and MANUS.Scientific reports · 2026Article
- Evaluation of ChatGPT's Accuracy, Repeatability, and Reasoning Ability in Prosthodontics Education: A Cross-Sectional Comparative Study with Prosthodontists.Journal of clinical and experimental dentistry · 2026Review
- Multimodal large language models for oral lesion diagnosis: a systematic review of diagnostic performance and clinical utility.Frontiers in oral health · 2026Review
- ChatGPT in Oral Pathology: Bright Promise or Diagnostic Mirage.Medicina (Kaunas, Lithuania) · 2025Article
- Visualization of artificial intelligence applications in oral disease diagnosis: A bibliometric analysis.Digital healthArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study evaluated the accuracy and repeatability of ChatGPT-4o, a multimodal AI model, in interpreting photographs of oral mucosal lesions, and explored its potential as a diagnostic support tool for specialists and non-specialists.
methodsThirty clinical photographs of oral and labial mucosal lesions were analysed using ChatGPT-4o. For each image, 30 responses were generated across 20 days. The model was asked to identify the anatomical location, suggest a diagnosis, and recommend diagnostic tests and treatments. Two oral pathology experts assessed 3600 responses using a three-point scale (0 = incorrect, 1 = partially correct, 2 = correct). Accuracy and repeatability were analysed using accuracy rates, Gwet's AC and percent agreement.
resultsChatGPT-4o achieved 71.4% accuracy in identifying lesion location and 58.2% in diagnosis. In cases with correct diagnoses, the model reached 90.7% and 95.8% accuracy in suggesting diagnostic tests and treatments, respectively. Repeated responses showed substantial to almost perfect agreement across all evaluated aspects.
conclusionsChatGPT-4o showed potential as a reliable and accessible tool to support the initial assessment of oral lesions. Although not a substitute for clinical judgment, it may enhance diagnostic efficiency, particularly in resource-limited settings. Further validation is needed before clinical use.
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