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Comparative Analysis of Diagnostic Accuracy and Case Difficulty Assessment of Three Large Language Models
Sponsor: Cairo University
Summary
This prospective, blinded diagnostic accuracy study aims to compare the performance of three large language models-ChatGPT (GPT-5.5 Pro), Gemini 3.1 Pro, and Claude Opus 4.7-in endodontic diagnosis and case difficulty assessment. The models will be evaluated against expert consensus as the reference standard using standardized clinical data and periapical radiographs. Diagnostic accuracy, sensitivity, specificity, and agreement with expert consensus will be assessed to determine the potential of LLMs as clinical decision-support tools in endodontics.
Official title: Comparative Analysis of Diagnostic Accuracy and Case Difficulty Assessment of Three Large Language Models in Endodontics Using Expert Consensus as the Reference Standard
Key Details
Gender
All
Age Range
16 Years - Any
Study Type
OBSERVATIONAL
Enrollment
342
Start Date
2026-09-01
Completion Date
2027-01
Last Updated
2026-07-29
Healthy Volunteers
Yes
Conditions
Interventions
ChatGPT (GPT-5.5 Pro), Gemini 3.1 Pro, Claude Opus 4.7
Large language model used to analyze standardized clinical information and periapical radiographs to provide pulpal and periapical diagnosis and endodontic case difficulty assessment according to AAE criteria