Patel Kishan, Huang Sherry, Rashid Arnav, Varghese Bino, Gholamrezanezhad Ali
Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Department of Urology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA.
Life (Basel). 2023 Oct 4;13(10):2011. doi: 10.3390/life13102011.
Artificial intelligence (AI) has been an important topic within radiology. Currently, AI is used clinically to assist with the detection of lesions through detection systems. However, a number of recent studies have demonstrated the increased value of neural networks in radiology. With an increasing number of screening requirements for cancers, this review aims to study the accuracy of the numerous AI models used in the detection and diagnosis of breast, lung, and prostate cancers. This study summarizes pertinent findings from reviewed articles and provides analysis on the relevancy to clinical radiology. This study found that whereas AI is showing continual improvement in radiology, AI alone does not surpass the effectiveness of a radiologist. Additionally, it was found that there are multiple variations on how AI should be integrated with a radiologist's workflow.
人工智能（AI）一直是放射学领域的一个重要话题。目前，AI在临床上通过检测系统用于辅助病变检测。然而，最近的一些研究已经证明了神经网络在放射学中的价值不断增加。随着对癌症筛查需求的不断增加，本综述旨在研究用于乳腺癌、肺癌和前列腺癌检测与诊断的众多AI模型的准确性。本研究总结了综述文章中的相关发现，并对其与临床放射学的相关性进行了分析。研究发现，虽然AI在放射学中显示出持续的进步，但仅靠AI并不超过放射科医生的有效性。此外，还发现AI与放射科医生工作流程的整合方式有多种变化。
