ArticleOdontology2026
Abstraction-dependent diagnostic performance of a multimodal foundation model in oral epithelial dysplasia.
Article in Odontology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Educational gaps and factors associated with artificial intelligence adoption among Egyptian periodontists: a multicenter cross-sectional study.Scientific reports · 2026Article
- Visualization of artificial intelligence applications in oral disease diagnosis: A bibliometric analysis.Digital healthArticle
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Authors and funding
5 authors.
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Abstract
The reliability of general-purpose multimodal large language models (LLMs) in oral histopathologic image interpretation remains incompletely characterized. To evaluate abstraction-level diagnostic competence, grading reliability, and structural error patterns of ChatGPT-5.2 in oral epithelial dysplasia histopathology. In this retrospective diagnostic accuracy study, ChatGPT-5.2 analyzed 200 digitized H&E-stained oral mucosal images (100 OPMD; 100 normal) in a zero-shot setting. Model outputs were compared with consensus diagnoses from three expert oral pathologists. The primary outcome was abstraction-level agreement (κ) across predefined WHO-aligned morphologic domains. Secondary outcomes included binary diagnostic accuracy, grade-stratified sensitivity, grading discordance, feature-level error profiling, inter-run stability, and modeled clinical utility. Agreement declined monotonically across morphologic abstraction domains (κ: 0.85 coarse morphology; 0.42 architectural; 0.09 high-risk cytologic; P[ordered] ≈ 1.000). Binary classification achieved a sensitivity 87.0% (95% CI 79.0-92.6) and a specificity 89.0% (95% CI 81.4-94.0). All severe dysplasia cases were detected (100%), with false negatives confined to non-severe lesions. Grading agreement was fair (weighted κ = 0.36) with predominant under-grading. Feature-level degradation was omission-dominant and concentrated within high-risk cytologic descriptors. Binary outputs demonstrated high inter-run stability (Fleiss' κ = 0.82). ChatGPT-5.2 demonstrated stable binary discrimination for OPMD detection but showed abstraction-dependent degradation in architectural and cytologic feature recognition central to dysplasia grading. While performance may support assistive or triage-oriented applications, grading variability and omission of high-risk cytologic criteria indicate that expert oversight remains essential. Further domain-specific validation is required before clinical integration.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.