ArticleBMC oral health2025
ChatGpt's accuracy in the diagnosis of oral lesions.
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.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- Performance of generative artificial intelligence in oral and maxillofacial radiology based on the board-certification examination of Japan.Oral radiology · 2026Article
- Diagnostic Performance of Contemporary Large Language Models on Free-Text Histopathologic Descriptions in Oral and Maxillofacial Pathology.Head and neck pathology · 2026Article
- Comparison of the diagnostic accuracy of dentists and ChatGPT in jawbone lesions.BMC oral health · 2026Article
- Factors influencing large language model adoption among dental students: a cross-sectional study.Scientific reports · 2026Article
- Accuracy of AI Tools in the Diagnosis of Benign, Potentially Malignant and Malignant Oral Lesions: A Pilot Study.Journal of clinical medicine · 2026Article
- Assessment of AI-Driven Large Language Models for Orthodontic Aesthetic Scoring Using the IOTN-AC.Diagnostics (Basel, Switzerland) · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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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.