Comparative Analysis of Diagnostic Accuracy and Case Difficulty Assessment of Three Large Language Models | NCT07732985

NCT07732985

Overview

NCT ID: NCT07732985
Lead Sponsor: Cairo UniversityOTHER
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
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.
Conditions: Pulp and Periapical Tissue Disease

Ages Eligible for Study: Child (0-17)Adult (18-64)Older Adult (65+)
Sexes Eligible for Study: All
Accepts Healthy Volunteers?: Yes
Eligibility Criteria: Inclusion Criteria:

Age above 16 years old.

Systemically healthy patient (ASA I or II).

Requiring endodontic treatment or retreatment

Patient's acceptance to participate in the study

Exclusion Criteria:

Medically compromised patients.

Pregnant women.

Traumatic dental injuries

Low quality periapical radiograph

Central Contacts: Mariam Ahmed HossamContact
mariam.ahmed.hosam@dentistry.cu.edu.eg00201110913251