Evidence map›Paper›PMID 40691556›Full record

ArticleBMC oral health2025

ChatGpt's accuracy in the diagnosis of oral lesions.

Pedram Hajibagheri, Sahba Khosousi Sani, Mohammad Samami, Rasoul Tabari-Khomeiran, Kiana Azadpeyma, Mohammad Khosousi Sani

Abstract read
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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
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

6 authors.

Pedram HajibagheriDental Sciences Research Center, Department of Oral and Maxillofacial Medicine, School of Dentistry, Guilan University of Medical Sciences, Rasht, Iran.ORCID https://orcid.org/0009-0005-7287-1942
Sahba Khosousi SaniDepartment of Oral and Maxillofacial Medicine, School of Dentistry, Guilan University of Medical Sciences, Rasht, Iran. Sahbakhosousisani@gmail.com.ORCID https://orcid.org/0000-0003-2074-9337
Mohammad SamamiDepartment of Oral and Maxillofacial Medicine, School of Dentistry, Guilan University of Medical Sciences, Rasht, Iran.ORCID https://orcid.org/0000-0002-4136-958X
Rasoul Tabari-KhomeiranSocial Determinants of Health Research Center, Guilan University of Medical Sciences, Rasht, Iran.ORCID https://orcid.org/0000-0002-7211-8660
Kiana AzadpeymaDepartment of Oral and Maxillofacial Medicine, School of Dentistry, Guilan University of Medical Sciences, Rasht, Iran.ORCID https://orcid.org/0009-0002-7715-668X
Mohammad Khosousi SaniOral & Maxillofacial Surgery Department, School of Dentistry, Guilan University of Medical Sciences, Rasht, Iran.ORCID https://orcid.org/0000-0001-9774-1881

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimChatGPT, a large language model (LLM) developed by OpenAI, is designed to generate human-like responses through the analysis of textual data. This study aimed to assess the accuracy and diagnostic capability of ChatGPT-4 in answering clinical scenario-based questions regarding oral lesions.

methodsThe study included 133 multiple-choice questions (MCQs), each consisting of five possible answers, randomly selected from the Clinical Guide to Oral Disease. Two oral medicine specialists reviewed the answers in the book to ensure accuracy. A general dentist categorized the questions into three levels of difficulty, and two oral medicine specialists validated these categorizations. At each level of difficulty, 37 questions were randomly selected. Consequently, the final questionnaire, consisting of a total of 111 questions categorized by difficulty level, was prepared. The process of asking questions began using the ''new message'' command, to minimize potential bias (influence of prior answers), the researchers manually cleared the chat history before presenting each new question.

resultChatGPT-4.0 demonstrated an accuracy rate of 97% for easy questions, 86.5% ± 34.6% for medium-level questions, and 78.4% ± 41.7% for difficult questions, with an overall accuracy rate of 87.4% ± 33.3.

conclusionAlthough ChatGPT-4.0 demonstrated satisfactory accuracy in answering clinical questions, its responses should not be exclusively relied upon for diagnostic purposes. Instead, the model should be utilized as a complementary tool under the supervision of clinicians in the diagnosis of oral lesions.

Indexed as

Mouth DiseasesGenerative Artificial IntelligenceHumansSurveys and QuestionnairesArtificial intelligenceChatGPTLarge Language modelMouth diseaseOral diagnosis

Identifiers

PMID40691556
PMCPMC12281746

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.