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Diagnostic Accuracy of Educated Large Language Models in Endodontic Diagnosis and Case Difficulty Assessment

NCT07706894 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 349

Last updated 2026-07-16

No results posted yet for this study

Summary

This diagnostic test accuracy (DTA) study aims to evaluate the diagnostic performance of educated large language models (Educated ChatGPT (GPT-5.5 Pro), Educated Gemini 3.1 Pro, and Educated Claude Opus 4.7) in endodontic practice. Their ability to establish pulpal and periapical diagnoses and assess endodontic case difficulty will be compared with the reference standard established by a panel of endodontic experts. Clinical and radiographic information from patients presenting for primary endodontic treatment or nonsurgical endodontic retreatment will be provided to both the AI models and the expert panel. The primary outcomes are the sensitivity, specificity, and the overall accuracy of the educated LLMs, with the objective of determining their potential role as reliable decision-support tools in endodontic diagnosis and treatment planning.

Conditions

  • Pulpal and Periapical Diseases

Interventions

DIAGNOSTIC_TEST

ChatGPT, Gemini, Calude

Three educated large language models (LLMs) will be evaluated in this study: Educated ChatGPT (GPT-5.5 Pro, OpenAI), Educated Gemini 3.1 Pro (Google), and Educated Claude Opus 4.7 (Anthropic).

Sponsors & Collaborators

  • Cairo University

    lead OTHER

Eligibility

Min Age
16 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-07-31
Primary Completion
2026-12-31
Completion
2027-06-30

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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 NCT07706894 on ClinicalTrials.gov