Clinicians face increasing workloads in medical imaging interpretation, and artificial intelligence (AI) offers potential relief. This meta-analysis evaluates the impact of human-AI collaboration on image interpretation workload. Four databases were searched for studies comparing reading time or quantity for image-based disease detection before and after AI integration. The Quality Assessment of Studies of Diagnostic Accuracy was modified to assess risk of bias. Workload reduction and relative diagnostic performance were pooled using random-effects model. Thirty-six studies were included. AI concurrent assistance reduced reading time by 27.20% (95% confidence interval, 18.22%–36.18%). The reading quantity decreased by 44.47% (40.68%–48.26%) and 61.72% (47.92%–75.52%) when AI served as the second reader and pre-screening, respectively. Overall relative sensitivity and specificity are 1.12 (1.09, 1.14) and 1.00 (1.00, 1.01), respectively. Despite these promising results, caution is warranted due to significant heterogeneity and uneven study quality.

临床医生在医学成像解释方面面临着越来越多的工作量，而人工智能 （AI） 提供了潜在的缓解。本荟萃分析评估了人类与 AI 协作对图像解释工作负载的影响。检索了四个数据库，以比较 AI 集成前后基于图像的疾病检测的读取时间或数量的研究。修改了诊断准确性研究的质量评估以评估偏倚风险。使用随机效应模型合并工作量减少和相对诊断性能。共纳入 36 项研究。AI 并发辅助将阅读时间缩短了 27.20%（95% 置信区间，18.22%–36.18%）。当 AI 作为第二个读者和预筛选时，阅读量分别下降了 44.47% （40.68%–48.26%） 和 61.72% （47.92%–75.52%）。总体相对敏感性和特异性分别为 1.12 （1.09， 1.14） 和 1.00 （1.00， 1.01）。尽管有这些有希望的结果，但由于显著的异质性和研究质量参差不齐，因此需要谨慎。