ArticleEuropean archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery2026
Comparative diagnostic accuracy of ChatGPT models in salivary gland disease: a multimodal vignette-based evaluation.
Article in European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Diagnostic Accuracy of Multimodal Large Language Models for Four-Class Benchmark of Oral Autoimmune Blistering Diseases: A Multicenter Paired Study.Diagnostics (Basel, Switzerland) · 2026Article
- Educational gaps and factors associated with artificial intelligence adoption among Egyptian periodontists: a multicenter cross-sectional study.Scientific reports · 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
- Evaluating the clinical safety of large language models in oral cancer-related patient communication: a repeated-prompt observational study.BMC oral health · 2026Observational
- Prompt engineering shapes diagnostic accuracy and explanation quality of LLM in oral lesion diagnosis: a prospective, expert-blinded benchmark study.Odontology · 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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7 authors.
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Abstract
backgroundThis study evaluated the diagnostic accuracy and consistency of ChatGPT-4o in salivary gland disorders compared to experienced clinicians.
methodsEighty anonymized salivary gland cases from peer-reviewed reports were evaluated by ChatGPT-4o using standardized multimodal prompts and by three oral medicine specialists who provided Top-5 differentials. The primary outcome was diagnostic accuracy at the most likely diagnosis (Top-1), within the top three (Top-3), and within the top five (Top-5) differential diagnoses, with agreement measured by Cohen's kappa and subgroup analyses by gland type, imaging, and case difficulty.
resultsAt Top-3 and Top-5, ChatGPT showed perfect sensitivity (100%) and Top-1 86.67%. Experts surpassed ChatGPT at Top-5 (77.5% vs. 67.5%, p < 0.0001), but ChatGPT outperformed experts at Top-1 (50.0% vs. 37.5%, p = 0.0309) and Top-3 (62.5% vs. 62.5%, p = 1.000). At Top-1, Cohen's Kappa indicated moderate agreement (0.55). Experts showed notable variation by modality (p = 0.0174) and gland (p = 0.053). Although initial subgroup analyses found no notable heterogeneity of ChatGPT's performance across imaging modalities, multivariate regression identified gland type to be an independent predictor of its Top-1 accuracy.
conclusionsThis first study shows ChatGPT can provide expert-level differential diagnoses for salivary gland disorders, suggesting promise as a supportive tool, though further research is needed to confirm its clinical role. CLINICAL SIGNIFICANCE: ChatGPT-4o shows promise as a reliable supportive tool for differential diagnosis in oral medicine. Compared to experts, it performed more consistently across imaging modalities, although the particular salivary gland involved had a significant impact on its accuracy. Further validation through larger studies is needed for its integration into routine clinical practice.
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