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) 的集成有望促进临床医生的工作，尤其是在临床成像方面。我们的目的是评估人工智能在医学成像中的应用对现实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评价。两名评审员对所有记录进行了双重筛选。对合格记录进行了方法学质量评估。感兴趣的结果是由于人工智能实施、任务时间变化和临床医生工作量而导致的工作流程适应。在筛选了 13,756 条记录后，我们确定了 48 项原始研究纳入审查。 33 项研究测量了任务时间，其中 67% 报告减少了。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的显着效果。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，人工智能充当检测任务的辅助读者。或者，人工智能被用作识别阳性病例的主要阅读器，从而重新组织工作列表或发出警报。只有三项研究根据人工智能使用节省的时间仔细计算了工作量。这项系统回顾和荟萃分析代表了对人工智能应用在现实世界临床成像中所提供的效率改进的评估，主要揭示了整个研究的增强。然而，现有研究中相当大的异质性对成像任务的整体有效性提供了强有力的推论。需要在标准化报告、系统集成评估和现实世界数据收集方面开展进一步的工作，以更好地了解人工智能在现实世界医疗保健工作流程中的技术进步。 系统审查注册：Prospero ID CRD42022303439，国际注册报告标识符（IRRID）：RR2-10.2196/40485。