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Simplifying Radiology Reports for Patients Using ChatGPT: A Systematic Review.

Michael Kwok1,2, Anthony Franklin3, Timothy Wastney4

  • 1Department of Diagnostic Radiology, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia.

Journal of Medical Imaging and Radiation Oncology
|February 18, 2026
PubMed
Summary

ChatGPT can simplify radiology reports for better patient understanding, but requires oversight. Further research is needed before widespread clinical adoption to ensure accuracy and patient satisfaction.

Keywords:
ChatGPTartificial intelligencegenerative artificial intelligencelarge language modelsradiology

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Radiology Communication

Background:

  • Radiology reports are often complex for patients.
  • Improving patient comprehension of medical information is crucial for informed decision-making.
  • Large language models offer potential solutions for simplifying medical text.

Purpose of the Study:

  • To systematically review the benefits and risks of using ChatGPT to simplify radiology reports.
  • To assess the impact of AI-driven simplification on report accuracy and completeness.
  • To evaluate the potential for enhancing patient understanding of radiological findings.

Main Methods:

  • Systematic review of studies published until December 17, 2024.
  • Searched PubMed/MEDLINE, Embase, Web of Science, and CENTRAL databases.
  • Included 16 studies evaluating ChatGPT (GPT-4, GPT-4o, GPT-3.5) for report simplification.

Main Results:

  • Simplified reports reduced reading grade levels from 9.6-14 to 5-9.4.
  • Factual accuracy ranged from 78%-100%, completeness from 83%-100%.
  • Potentially harmful errors decreased from 0%-36% to 0%-8% after outlier exclusion; only 19% assessed layperson comprehensibility.

Conclusions:

  • ChatGPT shows potential for patient-friendly radiology reports, but requires radiologist oversight and adherence to privacy laws.
  • Further prospective studies are essential to evaluate patient comprehension, satisfaction, and workflow impact before clinical implementation.