ArticleScientific reports2025
Utilizing GPT-4 to interpret oral mucosal disease photographs for structured report generation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Diagnostic performance of a multimodal large language model in assessing the index of complexity, outcome, and need: comparison with orthodontist evaluation.BMC oral health · 2026Article
- Diagnostic Performance of Contemporary Large Language Models on Free-Text Histopathologic Descriptions in Oral and Maxillofacial Pathology.Head and neck pathology · 2026Article
- Performance of large language models on image-based oral pathology questions from the Japanese National Dental Examination.Journal of dental sciences · 2026Article
- ChatGPT in Oral Pathology: Bright Promise or Diagnostic Mirage.Medicina (Kaunas, Lithuania) · 2025Article
- Application of image enhancement in the auxiliary diagnosis of oral potentially malignant disorders.Clinical oral investigations · 2025Article
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9 authors.
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Abstract
The aim of this study is to evaluate GPT-4's reasoning ability to interpret oral mucosal disease photos and generate structured reports from free-text inputs, while exploring the role of prompt engineering in enhancing its performance. Prompt received by utilizing automatic prompt engineering and knowledge of oral physicians, was provided to GPT-4 for generating structured reports based on cases of oral mucosal disease. The structured reports included 7 fine-grained items: "location", "shape", "number", "size", "clinical manifestation", "the border of the lesion" and "diagnosis". 120 cases were used for testing, which were divided into two datasets, textbook dataset and internet dataset. Oral physicians evaluated GPT-4's responses by confusion matrices, receiving recall and accuracy. ANOVA and Wald χ2 tests with Bonferroni correction were used to statistical analysis. A total of 120 cases of oral mucosal diseases were included, encompassing the following two datasets: textbook dataset (n = 60), internet dataset (n = 60). GPT-4 had higher recall with the textbook dataset compared to the internet dataset (90.73% vs 89.12%; P = .462, χ
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