人工智能在神经放射学部门的采用和实施现状:来自美国国家调查的见解
Max Wintermark1, Jason W Allen2, Rahul Bhala2
1From the Department of Neuroradiology (M.W.), The University of MD Anderson Center, Houston, TX; Department of Radiology and Imaging Sciences (J.W.A.), Indiana University School of Medicine, Indianapolis, IN; American Society of Neuroradiology (R.B.), Oak Brook, IL; Department of Radiology (C.D.), University of Virginia, Charlottesville, VA; Department of Radiology (A.G.), Columbia University Vagelos College of Physicians & Surgeons, New York, NY; Professor and Chair, Department of Radiology (C.P.H.), University of California San Francisco, San Francisco, CA; Professor of Radiology, Mayo Clinic College of Medicine & Science; Chair, Department of Radiology (J.M.H.), Mayo Clinic, Phoenix, AZ; Edward B. Singleton endowed Chair of Radiology (T.H.), Texas' Children's, Houston, TX; Professor and Chair of Radiology (M.V.J.), Warren Alpert School of Medicine at Brown University, Providence, RI; Department of Radiology and Biomedical Imaging (A.M., C.W.), Yale School of Medicine, New Haven CT; Shapiro Chair and Professor of Radiology (A.M.M.), University of Miami, Miller School of Medicine, Miami, FL; Department of Radiology (M.M.-B.), University of Alabama at Birmingham, Birmingham, AL; Kenneth L. and Gloria D. Krabbenhoft Chair and Professor of Radiology (B.P.), University of Iowa - Carver College of Medicine, Iowa City, IA; Radiologist-in-Chief and Lionel W. Young Chair in Radiology (T.Y.P.), Boston Children's Hospital; Professor of Radiology, Harvard Medical School; William P. Timmie Professor of Radiology (A.S.), Department of Radiology & Imaging Sciences, Emory University School of Medicine, Atlanta, GA; University of Cincinnati, Cincinnati (A.V.), OH and Professor and Chair of Radiology (C.W.), Radiologist-in-Chief of the Yale New Have Health System, Dorys McConnell Duberg Professor of Neuroscience, Assistant Dean for Translational Research. max.wintermark@gmail.com.
人工智能 (AI) 越来越多地用于神经放射学,主要用于中风应用. 成本和整合挑战等障碍仍然存在,但未来的采用预计将增加.
科学领域:
- 神经辐射学神经辐射学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 整合到医学成像中正在改变该领域.
- 神经放射学部门对人工智能技术的采用不均.
- 这项研究调查了人工智能的使用,工具,应用,障碍和美国神经放射学未来的期望.
研究的目的:
- 评估美国神经放射科部门人工智能采用的现状.
- 确定主要使用的应用程序和工具.
- 了解阻碍人工智能集成和未来前景的障碍.
主要方法:
- 横截面调查设计. 横截面调查设计.
- 19项调查问卷,采用混合格式的项目 (多选项,多选项,开放式).
- 用于分析调查数据的描述性统计.
主要成果:
- 81%的部门使用人工智能,主要用于与中风相关的应用.
- 其他应用包括报告生成,细分和图像增强.
- 采用的主要障碍是成本,整合挑战和效果证据不足.
结论:
- 人工智能在神经放射学中的应用正在增长,其中中风应用处于领先地位.
- 临床医生与供应商的合作对于克服采用障碍至关重要.
- 未来的AI整合旨在减少工作量并改善患者的治疗结果.


