Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts; MGH & BWH Center for Clinical Data Science, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.
Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts; MGH & BWH Center for Clinical Data Science, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.
J Am Coll Radiol. 2019 Sep;16(9 Pt B):1351-1356. doi: 10.1016/j.jacr.2019.06.010.
Recent advances in artificial intelligence (AI) are providing an opportunity to enhance existing clinical decision support (CDS) tools to improve patient safety and drive value-based imaging. We discuss the advantages and potential applications that may be realized with the synergy between AI and CDS systems. From the perspective of both radiologist and ordering provider, CDS could be significantly empowered using AI. CDS enhanced by AI could reduce friction in radiology workflows and can aid AI developers to identify relevant imaging features their tools should be seeking to extract from images. Furthermore, these systems can generate structured data to be used as input to develop machine learning algorithms, which can drive downstream care pathways. For referring providers, an AI-enabled CDS solution could enable an evolution from existing imaging-centric CDS toward decision support that takes into account a holistic patient perspective. More intelligent CDS could suggest imaging examinations in highly complex clinical scenarios, assist on the identification of appropriate imaging opportunities at the health system level, suggest appropriate individualized screening, or aid health care providers to ensure continuity of care. AI has the potential to enable the next generation of CDS, improving patient care and enhancing providers' and radiologists' experience.
人工智能(AI)的最新进展为增强现有的临床决策支持(CDS)工具提供了机会,以提高患者安全性并推动基于价值的成像。我们讨论了 AI 和 CDS 系统协同可能实现的优势和潜在应用。从放射科医生和开单医生的角度来看,AI 可以显著增强 CDS 的能力。AI 增强的 CDS 可以减少放射科工作流程中的摩擦,并帮助 AI 开发人员识别其工具应从图像中提取的相关成像特征。此外,这些系统可以生成结构化数据,作为输入来开发机器学习算法,从而推动下游护理路径。对于开单医生,人工智能支持的 CDS 解决方案可以使现有的以成像为中心的 CDS 向考虑到整体患者视角的决策支持发展。更智能的 CDS 可以在高度复杂的临床情况下建议进行成像检查,协助确定在医疗系统层面上的适当成像机会,建议进行适当的个体化筛查,或帮助医疗保健提供者确保患者的护理连续性。AI 有可能实现下一代 CDS,改善患者护理,并提高提供者和放射科医生的体验。