Wenderott Katharina, Krups Jim, Zaruchas Fiona, Weigl Matthias
Institute for Patient Safety, University Hospital Bonn, Bonn, Germany.
NPJ Digit Med. 2024 Sep 30;7(1):265. doi: 10.1038/s41746-024-01248-9.
In healthcare, integration of artificial intelligence (AI) holds strong promise for facilitating clinicians' work, especially in clinical imaging. We aimed to assess the impact of AI implementation for medical imaging on efficiency in real-world clinical workflows and conducted a systematic review searching six medical databases. Two reviewers double-screened all records. Eligible records were evaluated for methodological quality. The outcomes of interest were workflow adaptation due to AI implementation, changes in time for tasks, and clinician workload. After screening 13,756 records, we identified 48 original studies to be incuded in the review. Thirty-three studies measured time for tasks, with 67% reporting reductions. Yet, three separate meta-analyses of 12 studies did not show significant effects after AI implementation. We identified five different workflows adapting to AI use. Most commonly, AI served as a secondary reader for detection tasks. Alternatively, AI was used as the primary reader for identifying positive cases, resulting in reorganizing worklists or issuing alerts. Only three studies scrutinized workload calculations based on the time saved through AI use. This systematic review and meta-analysis represents an assessment of the efficiency improvements offered by AI applications in real-world clinical imaging, predominantly revealing enhancements across the studies. However, considerable heterogeneity in available studies renders robust inferences regarding overall effectiveness in imaging tasks. Further work is needed on standardized reporting, evaluation of system integration, and real-world data collection to better understand the technological advances of AI in real-world healthcare workflows. Systematic review registration: Prospero ID CRD42022303439, International Registered Report Identifier (IRRID): RR2-10.2196/40485.
在医疗保健领域，人工智能（AI）的整合有望极大地促进临床医生的工作，尤其是在临床成像方面。我们旨在评估医学成像中人工智能的实施对实际临床工作流程效率的影响，并对六个医学数据库进行了系统检索。两名评审员对所有记录进行了双重筛选。对符合条件的记录进行了方法学质量评估。感兴趣的结果包括人工智能实施导致的工作流程调整、任务时间的变化以及临床医生的工作量。在筛选了13756条记录后，我们确定了48项原始研究纳入综述。33项研究测量了任务时间，其中67%报告时间减少。然而，对12项研究进行的三项独立荟萃分析并未显示人工智能实施后有显著效果。我们确定了五种适应人工智能使用的不同工作流程。最常见的是，人工智能作为检测任务的二级阅片者。或者，人工智能被用作识别阳性病例的主要阅片者，从而导致重新组织工作列表或发出警报。只有三项研究基于通过人工智能使用节省的时间对工作量计算进行了审查。这项系统综述和荟萃分析评估了人工智能应用在实际临床成像中带来的效率提升，主要揭示了各项研究中的改进。然而，现有研究中存在相当大的异质性，使得难以对成像任务的总体有效性做出可靠推断。需要在标准化报告、系统集成评估和实际数据收集方面开展进一步工作，以更好地了解人工智能在实际医疗保健工作流程中的技术进步。系统综述注册：Prospero ID CRD42022303439，国际注册报告标识符（IRRID）：RR2-10.2196/40485。
