Evidence map›Paper›PMID 40281436›Full record

ArticleBMC oral health2025

Can deepseek and ChatGPT be used in the diagnosis of oral pathologies?

Ömer Faruk Kaygisiz, Mehmet Turhan Teke

Abstract readComparative Study
In one paragraph

Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed
–field-weighted citation impact
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

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.

3 · Its place in the literature

Who cites it

42 citing papers in PubMed.

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  5. Performance Comparison of a Domain-Specific Chatbot and General-Purpose Chatbots in Dental Traumatology.Dental traumatology : official publication of International Association for Dental Traumatology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ömer Faruk Kaygisiz *Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Gaziantep University, Gaziantep, Turkey. dtkaygisiz@gmail.com.
Mehmet Turhan Teke *Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Gaziantep University, Gaziantep, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveArtificial intelligence (AI) has been widely used in various medical fields to support diagnostic development. The development of different AI techniques has made important contributions to early diagnoses. This research compares and evaluates the diagnostic accuracy of ChatGPT-4o and Deepseek-v3 AI applications in 16 clinical case scenarios in oral pathologies. METHODOLOGY: Clinical case scenarios of 16 imaginary oral pathologies were prepared by the authors. The cases were asked to provide 3 possible preliminary diagnoses to two different AI applications, DeepSeek-V3 and ChatGPT-4o, and to reference the literature for these diagnoses. The diagnoses of both AI applications were evaluated with Likert scale by 20 different specialists from two different specialties.

resultsThe mean score for DeepSeek-v3 was 4.02 ± 0.36. For ChatGPT-4o it was 3.15 ± 0.41. According to the average scores, both models performed at a moderate to high level. Also, between the two AI models. DeepSeek-v3 was statistically better in 9 out of 16 clinical scenarios, while ChatGPT-4o was statistically better in 1 question. In general, DeepSeek-v3 was statistically more successful in the comparison of the two models (p = 0.024). In terms of references, ChatGPT-4o showed 62 references and 50 of them were fake, while 8 out of 48 references were fake in DeepSeek-v3.

conclusionsChatbot applications have the potential to become a valuable consultant for clinicians in the future thanks to its fast-processing ability. It is clear that it can help healthcare services by reducing the workload of clinicians. It can be said that the Deepseek-v3 model produces better results compared to ChatGPT-4o, but both applications need to be improved for routine use. It is thought that the release of versions of AI models that can only perform scans in the medical field and respond to clinicians by providing more reliable resources may make these models more valuable.

Indexed as

Artificial IntelligenceMouth DiseasesGenerative Artificial IntelligenceHumansArtificial intelligentChatGPT-4oDeepseek-V3Oral lesion

Identifiers

PMID40281436
PMCPMC12023442

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.