ArticleCureus2025
Comparative Evaluation of Diagnosis and Treatment Plan Given by Pediatric Dentists and Generated by ChatGPT: A Cross-Sectional Pilot Study.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Diagnostic accuracy of ChatGPT and teledentistry compared with dentist examination in detecting dental caries: preliminary diagnostic accuracy study.BMC oral health · 2026Article
- Generative Artificial Intelligence and Large Language Models in Paediatric Dentistry: A Scoping Review.International dental journal · 2026Article
- Performance comparison of large language models on pediatric dentistry questions in the Turkish dentistry specialization examination.BMC medical education · 2025Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is rapidly transforming healthcare, including dental specialties such as pediatric dentistry. Chat generative pre-trained transformer (ChatGPT), an AI language model, has demonstrated potential in patient communication and academic writing, but its clinical utility in diagnostic and treatment planning contexts remains underexplored.
aimTo evaluate the diagnostic accuracy and treatment planning capabilities of ChatGPT in comparison with those of experienced pediatric dentists. MATERIALS AND
methodsA cross-sectional pilot study was conducted involving 10 children aged 6-12 years. Standardized case scenarios were developed using clinical findings, medical history, and radiographs. Four pediatric dentists independently assessed each case and provided diagnoses and treatment plans. The same case scenarios were input into ChatGPT (v3.5), and its outputs were reviewed by a fifth pediatric dentist. A senior pediatric dentist blinded to the source validated all responses. Accuracy was compared, and inter-rater agreement was assessed using Cohen's kappa statistics.
resultsChatGPT achieved an 80% accuracy rate in diagnosis and treatment planning, equivalent to two pediatric dentists and slightly lower than the 90% accuracy seen in the other two. Statistical analysis revealed no significant difference between the performance of ChatGPT and the pediatric dentists (p = 0.926). Cohen's kappa indicated almost perfect agreement with two dentists (κ = 1.00) and moderate agreement with the others (κ = 0.615).
conclusionChatGPT demonstrated diagnostic accuracy comparable to pediatric dentists and may serve as a valuable adjunct in clinical pediatric dentistry. Larger studies are needed to confirm these findings and guide responsible integration of AI tools.
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