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

NCT07732985 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 342

Last updated 2026-07-29

No results posted yet for this study

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.

Conditions

  • Pulp and Periapical Tissue Disease

Interventions

DEVICE

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

Sponsors & Collaborators

  • Cairo University

    lead OTHER

Eligibility

Min Age
16 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-09-01
Primary Completion
2026-12-31
Completion
2027-01-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07732985 on ClinicalTrials.gov