ArticleOral and maxillofacial surgery2024
Concordance of clinician, Chat-GPT4, and ORAD diagnoses against histopathology in Odontogenic Keratocysts and tumours: a 15-Year New Zealand retrospective study.
Article in Oral and maxillofacial surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- JADE-Plus: A Multimodal Agentic Retrieval-Augmented Generation Large Language Framework for Diagnostic Support in Jawbone Lesions: Development and Technical Validation Study.Journal of imaging informatics in medicine · 2026Article
- JADE: jawbone lesion diagnosis and decision supporting system.Dento maxillo facial radiology · 2026Article
- Comparison of the diagnostic accuracy of dentists and ChatGPT in jawbone lesions.BMC oral health · 2026Article
- Artificial Intelligence in the Diagnosis of Odontogenous Cysts and Ameloblastomas-A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2026Review
- Diagnostic Accuracy of Clinical Decision Support Systems ORADIII and ORAD DDx to Histopathological Diagnosis of Jaw Lesions.Mayo Clinic proceedings. Digital health · 2026Article
- Article
- Impact of Transfer Learning on Convolutional Neural Networks for Odontogenic Tumor Diagnosis.Head and neck pathology · 2026Article
- Impact of AI-assisted decision support on radiological diagnosis of jawbone lesions.Clinical oral investigations · 2026Article
- Artificial intelligence for the detection and diagnosis of oral and maxillofacial lesions: evidence, limitations, and future directions.Brazilian oral research · 2026Review
- Evaluation of the Role of Artificial Intelligence-Assisted Diagnosis of Early-Stage Oral Cancer: A Clinical Study.Journal of pharmacy & bioallied sciences · 2025Article
- Exploring ChatGPT's potential in diagnosing oral and maxillofacial pathologies: a study of 123 challenging cases.BMC oral health · 2025Article
- Evaluating the Harmony Between Clinical Observations and Pathological Findings in Oral Soft Tissue Lesions: A Two-Decade Retrospective Study of Diagnostic Consistency.Journal of maxillofacial and oral surgery · 2025Article
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3 authors.
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Abstract
backgroundThis research aimed to investigate the concordance between clinical impressions and histopathologic diagnoses made by clinicians and artificial intelligence tools for odontogenic keratocyst (OKC) and Odontogenic tumours (OT) in a New Zealand population from 2008 to 2023.
methodsHistopathological records from the Oral Pathology Centre, University of Otago (2008-2023) were examined to identify OKCs and OT. Specimen referral details, histopathologic reports, and clinician differential diagnoses, as well as those provided by ORAD and Chat-GPT4, were documented. Data were analyzed using SPSS, and concordance between provisional and histopathologic diagnoses was ascertained.
resultsOf the 34,225 biopsies, 302 and 321 samples were identified as OTs and OKCs. Concordance rates were 43.2% for clinicians, 45.6% for ORAD, and 41.4% for Chat-GPT4. Corresponding Kappa value against histological diagnosis were 0.23, 0.13 and 0.14. Surgeons achieved a higher concordance rate (47.7%) compared to non-surgeons (29.82%). Odds ratio of having concordant diagnosis using Chat-GPT4 and ORAD were between 1.4 and 2.8 (p < 0.05). ROC-AUC and PR-AUC were similar between the groups (Clinician 0.62/0.42, ORAD 0.58/0.28, Char-GPT4 0.63/0.37) for ameloblastoma and for OKC (Clinician 0.64/0.78, ORAD 0.66/0.77, Char-GPT4 0.60/0.71).
conclusionClinicians with surgical training achieved higher concordance rate when it comes to OT and OKC. Chat-GPT4 and Bayesian approach (ORAD) have shown potential in enhancing diagnostic capabilities.
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