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An interpretable artificial intelligence (AI) model for COVID-19 detection on chest radiographs showed lower accuracy than radiologists. This AI diagnostic system requires further development to reach its full potential in clinical settings.

Keywords:
Application DomainClassificationDiagnosisInfectionLung

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Chest radiography is crucial for diagnosing COVID-19.
  • Artificial intelligence (AI) models offer potential for improving diagnostic efficiency.
  • Evaluating the real-time performance of AI in clinical settings is essential.

Purpose of the Study:

  • To prospectively evaluate the real-time performance of an interpretable AI model for COVID-19 detection on chest radiographs.
  • To compare the AI model's diagnostic accuracy with that of board-certified radiologists.

Main Methods:

  • A prospective observational study was conducted across 12 U.S. hospitals.
  • 95,363 chest radiographs were used for training, validation, and real-time testing.
  • AI model performance was assessed using ROC analysis, precision-recall curves, and F1 scores; comparisons were made with radiologist readings.

Main Results:

  • The AI model's real-time performance remained stable over 19 weeks (AUC, 0.70).
  • Model sensitivity varied by sex and race, with higher sensitivity in men and Asian/Black participants compared to women and White participants, respectively.
  • The AI system (63.5% accuracy) underperformed compared to radiologists (67.8% and 68.6% accuracy).

Conclusions:

  • Current AI-based tools for COVID-19 diagnosis on chest radiographs have not yet reached their full diagnostic potential.
  • AI models underperform compared to expert radiologists, indicating a need for further research and development.