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

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Radiologist Interaction with Artificial Intelligence-Generated Preliminary Reports: A Longitudinal Multireader Study.

Eun Kyoung Hong1, Chong-Hyun Suh2, Monika Nukala1

  • 1Department of Radiology, Mass General Brigham, Boston, Massachusetts.

Journal of the American College of Radiology : JACR
|September 22, 2025
PubMed
Summary
This summary is machine-generated.

Multimodal AI-generated reports enhanced radiology workflow efficiency and radiologist acceptance over time. However, report quality and agreement varied, especially for abnormal chest radiographs, necessitating continued human oversight.

Keywords:
Chest radiographsgenerative AIradiologist-AI interactionreport generation

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Radiology workflows are increasingly adopting AI tools.
  • Multimodal AI offers potential for improved diagnostic reporting.

Purpose of the Study:

  • To evaluate the integration of AI-generated reports in radiology workflow.
  • Assessing impact on radiologist efficiency, report acceptability, and quality over time.

Main Methods:

  • Multireader study of 756 chest radiographs with AI-generated reports.
  • Five radiologists interpreted images, with two assessing final reports for agreement and quality.
  • Analysis of reading times, acceptance rates, agreement, and quality scores across 7 batches.

Main Results:

  • Radiologist reading times decreased significantly over sequential batches (25.8s to 19.3s).
  • Acceptability of AI reports increased (54.6% to 60.2%), higher for normal (68.9%) than abnormal (52.6%) radiographs.
  • Agreement and quality scores were stable for normal but varied for abnormal cases.

Conclusions:

  • AI-generated reports improve chest radiograph interpretation efficiency and radiologist acceptance.
  • Variability in agreement and quality for abnormal cases highlights the need for expert human oversight.
  • AI integration requires careful monitoring to ensure diagnostic accuracy.