Li Matthew D, Little Brent P
Department of Radiology and Diagnostic Imaging, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada.
Mayo Clinic College of Medicine and Science, Department of Radiology, Division of Cardiothoracic Imaging, Mayo Clinic Florida, Florida; Committee Member, ACR Appropriateness Criteria Thoracic Imaging.
J Am Coll Radiol. 2023 Nov;20(11):1126-1130. doi: 10.1016/j.jacr.2023.04.019. Epub 2023 Jun 29.
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.
人工智能（AI）的使用者可能会过度依赖AI，从而对人机AI团队的表现产生负面影响。对于放射科医生在临床实践中常规使用解释性AI工具的未来而言，放射学教育需要不断发展，以便为放射科医生提供适当且明智地使用AI的技能。在这项工作中，我们研究了放射科实习生中过度依赖AI的情况是如何形成的，并探讨了如何缓解这一问题，包括通过使用AI强化教育。放射科实习生仍需要培养放射学基本的感知技能和知识掌握能力，以便安全地使用AI。我们借鉴人机交互研究的经验教训，提出了一个让放射科实习生以适当方式依赖AI工具的框架。
