Katzman Benjamin D, van der Pol Christian B, Soyer Philippe, Patlas Michael N
Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, L8S 3L8, Canada.
Department of Radiology, McMaster University, 1280 Main St. W., Hamilton, Ontario, L8S 3L8, Canada.
Diagn Interv Imaging. 2023 Jan;104(1):6-10. doi: 10.1016/j.diii.2022.07.005. Epub 2022 Aug 4.
Artificial intelligence (AI) applications in radiology have been rising exponentially in the last decade. Although AI has found usage in various areas of healthcare, its utilization in the emergency department (ED) as a tool for emergency radiologists shows great promise towards easing some of the challenges faced daily. There have been numerous reported studies examining the application of AI-based algorithms in identifying common ED conditions to ensure more rapid reporting and in turn quicker patient care. In addition to interpretive applications, AI assists with many of the non-interpretive tasks that are encountered every day by emergency radiologists. These include, but are not limited to, protocolling, image quality control and workflow prioritization. AI continues to face challenges such as physician uptake or costs, but is a long-term investment that shows great potential to relieve many difficulties faced by emergency radiologists and ultimately improve patient outcomes. This review sums up the current advances of AI in emergency radiology, including current diagnostic applications (interpretive) and applications that stretch beyond imaging (non-interpretive), analyzes current drawbacks of AI in emergency radiology and discusses future challenges.
在过去十年中，人工智能（AI）在放射学中的应用呈指数级增长。尽管人工智能已在医疗保健的各个领域得到应用，但其在急诊科作为急诊放射科医生的工具使用，有望缓解日常面临的一些挑战。已有大量报道研究探讨了基于人工智能的算法在识别常见急诊科病症以确保更快报告从而实现更快患者护理方面的应用。除了解读应用，人工智能还协助急诊放射科医生处理许多日常遇到的非解读任务。这些任务包括但不限于制定检查方案、图像质量控制和工作流程优先级排序。人工智能仍面临诸如医生接受度或成本等挑战，但它是一项长期投资，具有极大潜力缓解急诊放射科医生面临的诸多困难，并最终改善患者治疗结果。本综述总结了人工智能在急诊放射学中的当前进展，包括当前的诊断应用（解读性）和超越成像的应用（非解读性），分析了人工智能在急诊放射学中的当前缺点，并讨论了未来挑战。
