Appropriate Reliance on Artificial Intelligence in Radiology Education.

Journal: Journal of the American College of Radiology : JACR
Published Date:

Abstract

Users of artificial intelligence (AI) can become overreliant on AI, negatively affecting the performance of human-AI teams. For a future in which radiologists use interpretive AI tools routinely in clinical practice, radiology education will need to evolve to provide radiologists with the skills to use AI appropriately and wisely. In this work, we examine how overreliance on AI may develop in radiology trainees and explore how this problem can be mitigated, including through the use of AI-augmented education. Radiology trainees will still need to develop the perceptual skills and mastery of knowledge fundamental to radiology to use AI safely. We propose a framework for radiology trainees to use AI tools with appropriate reliance, drawing on lessons from human-AI interactions research.

Authors

  • Matthew D Li
    Department of Radiology, Harvard Medical School/Massachusetts General Hospital, Boston, Massachusets.
  • Brent P Little
    Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.