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Diagnostic Reliability of ChatGPT in Letournel-Judet Acetabular Fracture Classification

NCT07673991 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 184

Last updated 2026-06-29

No results posted yet for this study

Summary

This study aims to evaluate the diagnostic reliability of the multimodal artificial intelligence model ChatGPT-4o in classifying acetabular fractures using the Letournel-Judet classification system. The study retrospectively analyzed standardized pelvic radiographs (anteroposterior, iliac oblique, and obturator oblique) from 184 patients presenting with pelvic injuries. The diagnostic performance of ChatGPT-4o was compared against the independent assessments of two fourth-year orthopaedic residents and a reference standard established by an experienced trauma surgeon using multiplanar computed tomography (CT) and intraoperative findings. By utilizing a systematic radiographic checklist, the study assesses the AI (artificial intelligence) model's ability to identify key anatomical landmarks and integrate them into a final fracture pattern. This research aims to provide critical data on the current feasibility of using large language models as decision-support tools in complex orthopaedic trauma.

Conditions

  • Acetabular Fractures
  • Pelvic Injury

Interventions

OTHER

Diagnostic Assessment by ChatGPT-4o

No active intervention was performed; this study retrospectively analyzed radiographic images using a multimodal artificial intelligence model to assess its diagnostic accuracy compared to human clinicians.

Sponsors & Collaborators

  • Ankara City Hospital Bilkent

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-02-01
Primary Completion
2026-04-01
Completion
2026-05-12

Countries

  • Turkey (Türkiye)

Study Locations

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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 NCT07673991 on ClinicalTrials.gov