Ng Dianwen, Du Hao, Yao Melissa Min-Szu, Kosik Russell Oliver, Chan Wing P, Feng Mengling
Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore.
Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei.
Quant Imaging Med Surg. 2021 Jun;11(6):2775-2779. doi: 10.21037/qims-20-1083.
Advances in information technology have improved radiologists' abilities to perform an increasing variety of targeted diagnostic exams. However, due to a growing demand for imaging from an aging population, the number of exams could soon exceed the number of radiologists available to read them. However, artificial intelligence has recently resounding success in several case studies involving the interpretation of radiologic exams. As such, the integration of AI with standard diagnostic imaging practices to revolutionize medical care has been proposed, with the ultimate goal being the replacement of human radiologists with AI 'radiologists'. However, the complexity of medical tasks is often underestimated, and many proponents are oblivious to the limitations of AI algorithms. In this paper, we review the hype surrounding AI in medical imaging and the changing opinions over the years, ultimately describing AI's shortcomings. Nonetheless, we believe that AI has the potential to assist radiologists. Therefore, we discuss ways AI can increase a radiologist's efficiency by integrating it into the standard workflow.
信息技术的进步提高了放射科医生进行越来越多各种靶向诊断检查的能力。然而，由于老龄化人口对成像的需求不断增加，检查数量可能很快超过可供解读这些检查的放射科医生数量。然而，人工智能最近在涉及放射学检查解读的几个案例研究中取得了巨大成功。因此，有人提议将人工智能与标准诊断成像实践相结合以彻底改变医疗护理，最终目标是用人工智能“放射科医生”取代人类放射科医生。然而，医疗任务的复杂性常常被低估，许多支持者忽略了人工智能算法的局限性。在本文中，我们回顾了围绕医学成像中人工智能的炒作以及多年来不断变化的观点，最终描述了人工智能的缺点。尽管如此，我们相信人工智能有协助放射科医生的潜力。因此，我们讨论了通过将人工智能整合到标准工作流程中来提高放射科医生效率的方法。
