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Related Experiment Video

Updated: Jan 7, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

741

Evaluating Generative Artificial Intelligence as an Educational Tool for Radiology Resident Report Drafting.

Antonio Verdone1, Aidan Cardall2, Fardeen Siddiqui2

  • 1Department of Radiology, New York University Grossman School of Medicine, New York, New York.

Journal of the American College of Radiology : JACR
|December 26, 2025
PubMed
Summary
This summary is machine-generated.

A HIPAA-compliant GPT-4o system effectively provides automated feedback on radiology resident breast imaging reports. This AI tool shows high agreement with attending radiologists and is rated as helpful, supporting radiology education.

Keywords:
Breast imaginggenerative AIlarge language modelradiology education

Related Experiment Videos

Last Updated: Jan 7, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

741

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiology Education

Background:

  • Radiology residents need timely, personalized feedback for skill development.
  • Clinical workloads limit attending radiologists' ability to provide comprehensive guidance.
  • Automated feedback systems can potentially bridge this gap in real-time.

Purpose of the Study:

  • To evaluate a HIPAA-compliant GPT-4o system for automated feedback on resident breast imaging reports.
  • To assess the accuracy and helpfulness of AI-generated feedback compared to attending radiologists.
  • To determine the potential of AI as a scalable tool in radiology education.

Main Methods:

  • Analysis of 5,000 resident-attending breast imaging report pairs.
  • GPT-4o was prompted to identify common errors in resident reports.
  • A reader study with 4 attending radiologists and 4 residents evaluated GPT-4o's feedback on 100 report pairs.

Main Results:

  • GPT-4o demonstrated strong agreement with attending consensus on error identification (90.5%, 78.3%, 90.4%).
  • AI feedback was rated as helpful in the majority of cases (89.8%, 83.0%, 92.0%).
  • Replacing human readers with GPT-4o did not significantly alter inter-reader agreement.

Conclusions:

  • GPT-4o reliably identifies key educational errors in radiology resident reports.
  • The AI system shows potential as a scalable tool to enhance radiology education.
  • Automated feedback can supplement traditional mentorship in radiology training.