ArticleOdontology2026
Clinical decision accuracy in endodontic treatment of patients with systemic diseases: a comparative analysis using different artificial intelligence models.
Article in Odontology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to compare the clinical decision-making accuracy of different artificial intelligence (AI) models in endodontic treatment planning for patients with systemic diseases. A scenario-based, cross-sectional educational study was conducted using 40 standardized clinical scenarios representing ten commonly encountered systemic conditions affecting endodontic care. Scenarios were developed based on international endodontic and medical guidelines and reviewed by medical specialists and experienced endodontists. Four AI models, ChatGPT-5.1, Gemini 2.5 Pro, Gemini 2.5 Flash, and ChatGPT-3.5, were queried using identical, standardized prompts within fully isolated interaction environments to prevent contextual memory effects. AI-generated responses were independently evaluated by two calibrated endodontists using a predefined 10-point scoring system across four clinical domains. Clinical accuracy was categorized as high, partial, or incorrect. Nonparametric statistical analyses were performed. No statistically significant differences were observed among AI models in overall clinical decision accuracy or domain-specific scores (Friedman test, p > 0.05). Although categorical analysis revealed an overall difference in the proportion of high-accuracy responses (Cochran's Q, p = 0.007), post hoc comparisons did not demonstrate significant pairwise differences. Deviation analysis revealed comparable proximity of all models to the expert-defined optimal decisions, with greater variability observed for the Gemini 2.5 Flash. Current AI models demonstrate comparable clinical decision-making performance in endodontic scenarios involving medically compromised patients. While descriptive trends were observed, no single model consistently outperformed others. AI systems may serve as supportive decision-making tools when used under professional supervision, but should not replace clinical judgment.
Indexed as
Identifiers
41670838What Socratic 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 Socratic graph.