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Generative Artificial Intelligence for Chest Radiograph Interpretation in the Emergency Department.

Jonathan Huang1,2,3, Luke Neill1, Matthew Wittbrodt4

  • 1Department of Emergency Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois.

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|October 5, 2023
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Summary
This summary is machine-generated.

Generative artificial intelligence (AI) produced radiology reports comparable in quality to human radiologists for emergency department chest X-rays. This AI shows potential for improving diagnostic accuracy and efficiency in emergency care.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Multimodal generative artificial intelligence (AI) offers potential for optimizing emergency department (ED) care through automated radiology report generation.
  • Evaluating AI's accuracy and quality in interpreting ED chest radiographs is crucial for clinical integration.

Purpose of the Study:

  • To assess the accuracy and quality of AI-generated chest radiograph interpretations in an emergency department setting.
  • To compare AI interpretations with those of on-site radiologists and teleradiology services.

Main Methods:

  • A retrospective diagnostic study analyzed 500 ED chest radiographs.
  • Interpretations from AI, on-site radiologists, and teleradiology were rated by emergency physicians on a 5-point Likert scale.
  • Statistical models were used to compare Likert scores and the probability of clinically significant discrepancies.

Main Results:

  • AI and radiologist reports received significantly higher ratings than teleradiology reports (P < .001).
  • AI and radiologist reports showed no significant difference in quality ratings.
  • No significant differences in the probability of clinically significant discrepancies were found between AI, radiologist, and teleradiology reports across various findings.

Conclusions:

  • Generative AI models produce chest radiograph reports with clinical accuracy and textual quality comparable to human radiologists.
  • AI reports demonstrated superior textual quality compared to teleradiology reports.
  • Clinical implementation of AI could enhance timely detection of critical findings and aid imaging interpretation in the ED.