Journal of endodontics · 2026
Comparing Diagnostic Ability of Two Off-the-Shelf Large Language Models with That of Endodontists for Teeth Requiring Initial Root Canal Treatment: A Multicenter Retrospective Investigation.
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Torabinejad Mahmoud, Tordik Patricia A, Alenezi Mona, Parirokh Massoud, Dianat Omid, Foroughi Max, Zegar Zegar, Oyoyo Udochukwu E
Аннотация
INTRODUCTION: Despite growing interest in large language models (LLMs) for endodontic diagnosis, prior studies have used simulated cases, examination formats, or small selected series. This multicenter retrospective study compared the diagnostic accuracy of ChatGPT-5.2 and Gemini 3.0 with that of calibrated endodontists across 645 teeth requiring initial root canal treatment.
METHODS: Cases were evaluated across four endpoints: pulpal diagnosis, periapical diagnosis, treatment planning, and case difficulty. Standardized clinical summaries and radiographs were submitted to endodontists and both LLMs using a fixed single-pass protocol. Exact-match accuracy, Cohen's κ, Gwet's AC1, macro-averaged F1 score, McNemar tests, and generalized estimating equations were used for statistical comparison.
RESULTS: Endpoint accuracies ranged from 74% to 88%. Macro F1 was highest for periapical diagnosis (ChatGPT-5.2, 0.724; Gemini 3.0, 0.719) and lowest for treatment planning (0.385; 0.550), where both models showed lower observed performance in minority categories, interpreted as suggestive given small subgroup sizes. Composite accuracy across the three clinical endpoints was 61.1% (95% CI, 57.4%-64.8%) and 59.4% (95% CI, 55.3%-63.3%), and four-endpoint accuracy was 47.3% (95% CI, 43.3%-51.2%) and 46.2% (95% CI, 42.3%-49.8%), indicating at least one clinically relevant error in about half of cases. Treatment planning was the only endpoint differing between models after Holm-Bonferroni correction, favoring Gemini 3.0 (OR, 1.457; adjusted p = .003).
CONCLUSIONS: Both models performed acceptably on individual endpoints and common presentations, but performance was endpoint-specific and substantially reduced on integrated assessment and low-prevalence categories. These findings support use of LLMs in endodontic diagnosis and treatment planning as adjunctive tools, but not without expert supervision.
METHODS: Cases were evaluated across four endpoints: pulpal diagnosis, periapical diagnosis, treatment planning, and case difficulty. Standardized clinical summaries and radiographs were submitted to endodontists and both LLMs using a fixed single-pass protocol. Exact-match accuracy, Cohen's κ, Gwet's AC1, macro-averaged F1 score, McNemar tests, and generalized estimating equations were used for statistical comparison.
RESULTS: Endpoint accuracies ranged from 74% to 88%. Macro F1 was highest for periapical diagnosis (ChatGPT-5.2, 0.724; Gemini 3.0, 0.719) and lowest for treatment planning (0.385; 0.550), where both models showed lower observed performance in minority categories, interpreted as suggestive given small subgroup sizes. Composite accuracy across the three clinical endpoints was 61.1% (95% CI, 57.4%-64.8%) and 59.4% (95% CI, 55.3%-63.3%), and four-endpoint accuracy was 47.3% (95% CI, 43.3%-51.2%) and 46.2% (95% CI, 42.3%-49.8%), indicating at least one clinically relevant error in about half of cases. Treatment planning was the only endpoint differing between models after Holm-Bonferroni correction, favoring Gemini 3.0 (OR, 1.457; adjusted p = .003).
CONCLUSIONS: Both models performed acceptably on individual endpoints and common presentations, but performance was endpoint-specific and substantially reduced on integrated assessment and low-prevalence categories. These findings support use of LLMs in endodontic diagnosis and treatment planning as adjunctive tools, but not without expert supervision.