Evidence map›Paper›PMID 42151995›Full record

ArticleBMC oral health2026

Comparison of the diagnostic accuracy of dentists and ChatGPT in jawbone lesions.

Nezahat Sena Satan, Umut Pamukçu, Barış Erkut Türk, Zühre Akarslan, Sibel Açık Kemaloğlu, Ilkay Peker

Abstract readComparative Study
In one paragraph

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.

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1 · What the graph read from it

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.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Nezahat Sena SatanDepartment of Dentomaxillofacial Radiology, Gazi University Faculty of Dentistry, Bishkek St. (8th St.) 1st St. No:8, Emek, Ankara, 06490, Turkey. nezahatsatan@gazi.edu.tr.ORCID 0000-0002-6384-1136
Umut PamukçuDepartment of Dentomaxillofacial Radiology, Gazi University Faculty of Dentistry, Bishkek St. (8th St.) 1st St. No:8, Emek, Ankara, 06490, Turkey.ORCID 0000-0001-8356-8344
Barış Erkut TürkDepartment of Dentomaxillofacial Surgery, Gazi University Faculty of Dentistry, Ankara, Turkey.ORCID 0000-0002-4025-1133
Zühre AkarslanDepartment of Dentomaxillofacial Radiology, Gazi University Faculty of Dentistry, Bishkek St. (8th St.) 1st St. No:8, Emek, Ankara, 06490, Turkey.ORCID 0000-0001-9237-412X
Sibel Açık KemaloğluDepartment of Statistics, Ankara University Faculty of Science Department of Statistics, Ankara, Turkey.ORCID 0000-0003-0449-6966
Ilkay PekerDepartment of Dentomaxillofacial Radiology, Autism and Developmental Disorders Research Center, Gazi University Faculty of Dentistry, Ankara, Turkey.ORCID 0000-0002-2888-2979

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

objectivesArtificial intelligence (AI) is leading to a significant paradigm shift in medical imaging and diagnostic sciences. In particular, Chat Generative Pre-trained Transformer (ChatGPT) is finding increasing application in diagnostic processes due to its ability to generate clinical outcomes. This study aims to evaluate the diagnostic accuracy of ChatGPT for jawbone lesions and also to compare it with that of Oral and Maxillofacial Radiologists (OMFR), Oral and Maxillofacial Surgeons (OMFS), and general dentists. MATERIALS &

methodsThirty cases with jawbone lesions, for which clinical information, panoramic radiographs, and histopathological diagnoses were available, were selected. A questionnaire was prepared, including participants' (OMFR, OMFS, and general dentists) demographic information, the cases' clinical findings and panoramic radiographs, and distributed via electronic communication channels. The same cases were loaded into ChatGPT-4 and asked to generate a preliminary diagnosis. The data were statistically analyzed using the Wilcoxon Signed Rank, Mann-Whitney U, and Kruskal-Wallis tests at a significance level of p < 0.05.

resultsOverall, ChatGPT's diagnostic accuracy was limited to 46.67%, while the OMFR (67.71%) and OMFS (58.96%) groups had statistically significantly higher success rates (p < 0.05) than ChatGPT. General dentists (37.85%) had lower or similar diagnostic accuracy compared to ChatGPT in most subgroups (gender, age, workplace, professional experience).

conclusionsChatGPT demonstrated moderate diagnostic accuracy. While OMFR and OMFS participants had significantly higher accuracy rates than ChatGPT, ChatGPT generally outperformed general dentists. These results indicate that such AI systems cannot replace specialist clinicians but can provide valuable contributions as supportive tools that enhance diagnosis.

Indexed as

DentistsJaw DiseasesAdultFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedOral and Maxillofacial SurgeonsRadiography, PanoramicSurveys and QuestionnairesArtificial intelligence in healthcareChatGPTDiagnosisJawbone LesionLarge Multimodal Model (LMM)Maxillofacial radiology

Identifiers

PMID42151995
PMCPMC13397687

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LicenceCC BY-NC-ND
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Registered trials

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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.