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TRUVACE RECORD VERSION
record: TRV-2026-1352
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-14T18:14:02.938182Z
status: published
lens: trace
sector: health
headline: 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
dek: 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. Stan…
gain_title: On individual endpoints such as pulpal or periapical diagnosis, both LLMs reached 74% to 88% accuracy on real root canal cases, which supports a role as adjunctive tools.
problem_title: When the endpoints were combined, both models made at least one clinically relevant error in about half of cases, and performance dropped in low-prevalence categories.
trace_subject: LLM diagnosis and treatment planning for teeth needing initial root canal treatment
gain_reading: On individual endpoints such as pulpal or periapical diagnosis, both LLMs reached 74% to 88% accuracy on real root canal cases, which supports a role as adjunctive tools.
gain_evidence: Both models performed acceptably on individual endpoints and common presentations
problem_reading: When the endpoints were combined, both models made at least one clinically relevant error in about half of cases, and performance dropped in low-prevalence categories.
problem_evidence: indicating at least one clinically relevant error in about half of cases | substantially reduced on integrated assessment and low-prevalence categories
quick_read: Researchers at multiple centers retrospectively tested ChatGPT-5.2 and Gemini 3.0 against calibrated endodontists on 645 teeth needing a first root canal. Each case, supplied as a standardized clinical summary with radiographs, was assessed once for pulpal diagnosis, periapical diagnosis, treatment plan and case difficulty.

The results suggest that these general-purpose tools are useful for narrow tasks but unreliable as a whole-case assessor, so any clinical use needs expert oversight. Findings on rarer categories are only suggestive because the subgroups were small, and the work is retrospective rather than a test in live care.
limitation: Findings on minority categories are described as suggestive because of small subgroup sizes, and the study is retrospective.
tag: Dual reading
key_points: Single-endpoint accuracy ranged from 74% to 88% across the two models. | Composite accuracy over the three clinical endpoints was 61.1% for ChatGPT-5.2 and 59.4% for Gemini 3.0. | Four-endpoint accuracy fell to 47.3% and 46.2% respectively. | Only treatment planning differed between models after correction, favoring Gemini 3.0 (OR 1.457, adjusted p = .003). | Macro F1 for treatment planning was 0.385 for ChatGPT-5.2 and 0.550 for Gemini 3.0.
rundown: The study addressed a gap in earlier work, which relied on simulated cases, examination-style questions or small selected series. Here the sample was 645 real teeth from several centers, and the models were compared with calibrated endodontists under a fixed single-pass protocol.

Statistical comparison used exact-match accuracy, Cohen's kappa, Gwet's AC1, macro-averaged F1, McNemar tests and generalized estimating equations. Macro F1 peaked for periapical diagnosis at 0.724 for ChatGPT-5.2 and 0.719 for Gemini 3.0.

Both models did worse in minority categories of treatment planning, which the authors call suggestive given small subgroup sizes. The authors conclude that LLMs can serve as adjunctive tools in endodontic diagnosis and planning, but not without expert supervision.
sources:
- peer_reviewed | Journal of Endodontics | https://doi.org/10.1016/j.joen.2026.10.003 | 2026-10-13
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