ArticleInternational dental journal2026
A Multimodal Large Language Model Framework for Clinical Subtyping and Malignant Transformation Risk Prediction in Oral Lichen Planus: A Paired Comparison With Expert Clinicians.
Article in International dental journal, 2026. 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.
- Efficacy of topical erythropoietin gel and mouthwash for recurrent aphthous stomatitis in a randomized clinical trial.Scientific reports · 2026Trial
- Diagnostic Accuracy of Multimodal Large Language Models for Four-Class Benchmark of Oral Autoimmune Blistering Diseases: A Multicenter Paired Study.Diagnostics (Basel, Switzerland) · 2026Article
- Trajectory-aware risk stratification of oral lichen planus using a multimodal large language model: a longitudinal diagnostic accuracy study.Scientific reports · 2026Article
- Educational gaps and factors associated with artificial intelligence adoption among Egyptian periodontists: a multicenter cross-sectional study.Scientific reports · 2026Article
- Abstraction-dependent diagnostic performance of a multimodal foundation model in oral epithelial dysplasia.Odontology · 2026Article
- Risk classification of oral potentially malignant disorders (OPMDs) using explainable machine learning through a community-based cross-sectional study in rural India.BMC public health · 2026Article
- Prompt engineering shapes diagnostic accuracy and explanation quality of LLM in oral lesion diagnosis: a prospective, expert-blinded benchmark study.Odontology · 2026Article
- Artificial Intelligence Versus Human Dental Expertise in Diagnosing Periapical Pathosis on Periapical Radiographs: A Multicenter Study.Bioengineering (Basel, Switzerland) · 2026Article
- Calibration of AI large language models with human subject matter experts for grading of clinical short-answer responses in dental education.BMC oral health · 2026Article
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5 authors.
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Abstract
backgroundOral lichen planus (OLP), oral lichenoid lesions (OLL), and squamous cell carcinoma on a lichenoid background (SCC-over-LP/LLP) overlap clinically, delaying malignant transformation recognition.
objectiveTo evaluate a multimodal large language model (ChatGPT-5) against oral medicine (OM) specialists for tripartite classification (OLP/OLL/SCC-over-LP/LLP) and malignant-risk flagging.
methodsCross-sectional, paired diagnostic accuracy study adhering to STARD/STARD-AI. Retrospective, anonymized cases (n = 262; OLP = 100, OLL = 100, SCC-over-LP/LLP = 62) were independently evaluated by ChatGPT-5 and a comparator panel of board-certified OM specialists using identical clinical histories and intraoral photographs (no histopathology provided to either). A separate reference standard panel (three OM experts) established the diagnosis using full clinical data and histopathology prior to index testing. PRIMARY OUTCOME: paired accuracy (McNemar). Secondary: certainty (1-5), management agreement (Gwet's AC1), and recognition of malignant red-flag features.
resultsOverall accuracy was comparable (84.7% ChatGPT-5 vs 85.5% OM specialists; McNemar P = .856, Cohen's h = 0.03). Sensitivity was high for OLP 0.99 and SCC-over-LP/LLP 0.85; OLL sensitivity 0.70 with specificity 1.00. Biopsy/referral agreement was near-perfect (AC1 = 0.91). Malignant-risk features were correctly identified in 88% of SCC-over-LP/LLP cases by ChatGPT-5 vs 92% by OM specialists (P = .41).
conclusionsA multimodal large language model can reach expert-level accuracy for OLP/OLL/SCC-over-LP/LLP and reliably flag malignant transformation risk, supporting its role as an adjunctive decision-support tool in OM.
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