ArticleFrontiers in bioinformatics2026
Diagnostic accuracy and repeatability of ChatGPT using textual and radiographic data in reversible pulpitis: a retrospective diagnostic study.
Article in Frontiers in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Large language models have recently evolved into multimodal systems capable of interpreting images alongside text. The aim of this study was to assess the diagnostic accuracy and repeatability of ChatGPT-5 for reversible pulpitis, based on structured clinical information and periapical radiographs (PRs). Methods: Thirty expert-validated cases of reversible pulpitis were retrospectively selected. For each case, clinical data and PRs were compiled to create a reference dataset, with expert consensus serving as the reference standard. A structured prompt incorporating clinical information and PR was designed and included six predefined questions: four addressing diagnostic reasoning, one assessing whether PR information was used to establish the diagnosis, and one requiring radiographic findings description. Each case was entered thirty times into ChatGPT-5, generating a total of 900 answers. Answers were assessed by two independent experts, with disagreements resolved by a third. Radiographic descriptions were graded using a three-point Likert scale. Diagnostic accuracy and repeatability were analysed using binomial and agreement methods. Results: Diagnostic questions related to tooth identification, pulpal diagnosis, periapical diagnosis and treatment indication showed accuracies above 96%. Overall accuracy for radiographic description was low (4.67%). Coronal radiographic interpretation showed poor performance (5.15%), whereas accuracy for pulpal and periapical structures exceeded 96%. Repeatability was almost perfect for diagnostic questions and pulpal/periapical interpretation, but moderate for coronal descriptions. Discussion: ChatGPT-5 demonstrated high accuracy and repeatability in text-based diagnostic reasoning for reversible pulpitis. However, limitations in radiographic interpretation, particularly of coronal structures, indicate that its use should be limited to a supervised decision-support role.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.