Department of Radiology, Stanford University School of Medicine, Center for Academic Medicine, Palo Alto, CA.
Department of Radiology, Stanford University School of Medicine, Center for Academic Medicine, Palo Alto, CA.
Semin Ultrasound CT MR. 2024 Apr;45(2):152-160. doi: 10.1053/j.sult.2024.02.004. Epub 2024 Feb 23.
Artificial intelligence's (AI) emergence in radiology elicits both excitement and uncertainty. AI holds promise for improving radiology with regards to clinical practice, education, and research opportunities. Yet, AI systems are trained on select datasets that can contain bias and inaccuracies. Radiologists must understand these limitations and engage with AI developers at every step of the process - from algorithm initiation and design to development and implementation - to maximize benefit and minimize harm that can be enabled by this technology.
人工智能（AI）在放射学领域的出现既令人兴奋又令人感到不确定。AI 有望改善临床实践、教育和研究机会方面的放射学。然而，AI 系统是在精选的数据集上进行训练的，这些数据集可能包含偏差和不准确。放射科医生必须了解这些局限性，并在 AI 开发的每个步骤中与开发人员合作，从算法启动和设计到开发和实施，以最大限度地发挥这项技术的益处，减少其可能带来的危害。
