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Clinical Trial

Comparative Analysis of Diagnostic Accuracy and Case Difficulty Assessment of Three Large Language Models

NCT: NCT07732985 · NOT_YET_RECRUITING

NCT IDNCT07732985
StatusNOT_YET_RECRUITING
Start Date2026-09-01
SponsorCairo University (OTHER)
First posted
Last updated

Brief 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 Source

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