ArticleBMC oral health2026
Diagnostic performance of multimodal large language models in detecting pulp stones on panoramic radiographs.
Article in BMC oral health, 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPulp stones are calcified structures within the dental pulp that may increase the complexity of endodontic treatment and are commonly identified through radiographic examinations. Despite recent advances in artificial intelligence applied to dental imaging, the diagnostic capability of publicly available multimodal large language models (LLMs) for detecting pulp stones under zero-shot conditions remains unclear. Therefore, this study evaluated the diagnostic performance and response consistency of multimodal LLMs in detecting pulp stones on panoramic radiographs of posterior teeth.
methodsThis comparative and exploratory study evaluated the GPT-5 and Gemini 2.5 Flash multimodal LLMs using panoramic radiograph image crops containing posterior teeth. A total of 64 image crops, comprising 192 posterior teeth, were analyzed. The models were tested under standardized zero-shot conditions using independent sessions and a standardized English-language prompt. Each image was submitted six times to both models to assess response consistency. Diagnostic performance was evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score with 95% confidence intervals. Intra-model consistency was assessed using majority agreement, complete agreement across repetitions, and Fleiss' Kappa coefficient.
resultsThe models demonstrated similar overall accuracy for binary detection of pulp stones, with 56.5% for ChatGPT and 57.0% for Gemini. ChatGPT showed high specificity (96.2%) and greater intra-model stability, whereas Gemini demonstrated higher sensitivity (81.8%) and superior performance in identifying the affected tooth and dental group. However, Gemini also presented lower specificity and greater variability across repeated evaluations. Fleiss' Kappa coefficients indicated low intra-model agreement beyond chance for both models, particularly for Gemini.
conclusionMultimodal LLMs demonstrated limited capability in detecting pulp stones on panoramic radiographs, with distinct diagnostic profiles between the evaluated models. Although the findings suggest potential future applications of LLMs as auxiliary tools for dental image interpretation, their current performance does not yet support consistent clinical applicability for pulp stone detection.
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.