Evaluating the Quality and Understandability of Radiology Report Summaries Generated by ChatGPT: Survey Study.

Journal: JMIR formative research
Published Date: Aug 27, 2025

Abstract

BACKGROUND: Radiology reports convey critical medical information to health care providers and patients. Unfortunately, they are often difficult for patients to comprehend, causing confusion and anxiety, thereby limiting patient engagement in health care decision-making. Large language models (LLMs) like ChatGPT (OpenAI) can create simplified, patient-friendly report summaries to increase accessibility, albeit with errors.

Authors

  • Alexis Sunshine
    Department of Radiology, University of Colorado Anschutz Medical Campus, 19th Ave. Mail Stop C278, Aurora, CO, 80045, United States, 1 303-724-3796, 1 303-724-3795.
  • Grace H Honce
    Hartway Evaluation Group, Denver, CO, United States.
  • Andrew L Callen
    Department of Radiology, University of Colorado Anschutz Medical Campus, Denver, CO, USA. andrew.callen@cuanschutz.edu.
  • David A Zander
    Department of Radiology, University of Colorado Anschutz Medical Campus, 19th Ave. Mail Stop C278, Aurora, CO, 80045, United States, 1 303-724-3796, 1 303-724-3795.
  • Jody L Tanabe
    Department of Radiology, University of Colorado Anschutz Medical Campus, 19th Ave. Mail Stop C278, Aurora, CO, 80045, United States, 1 303-724-3796, 1 303-724-3795.
  • Samantha L Pisani Petrucci
    Department of Radiology, University of Colorado Anschutz Medical Campus, 19th Ave. Mail Stop C278, Aurora, CO, 80045, United States, 1 303-724-3796, 1 303-724-3795.
  • Chen-Tan Lin
    Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA.
  • Justin M Honce
    Department of Radiology, University of Colorado Anschutz Medical Campus, 19th Ave. Mail Stop C278, Aurora, CO, 80045, United States, 1 303-724-3796, 1 303-724-3795.

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