Chen Mingyang, Wang Yuting, Wang Qiankun, Shi Jingyi, Wang Huike, Ye Zichen, Xue Peng, Qiao Youlin
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
NPJ Digit Med. 2024 Nov 30;7(1):349. doi: 10.1038/s41746-024-01328-w.
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.
临床医生在医学影像解读方面面临着日益增加的工作量，而人工智能（AI）有望缓解这一状况。这项荟萃分析评估了人机协作对影像解读工作量的影响。检索了四个数据库，以查找比较人工智能整合前后基于图像的疾病检测的阅读时间或数量的研究。对诊断准确性研究的质量评估进行了修改，以评估偏倚风险。使用随机效应模型汇总工作量减少情况和相对诊断性能。纳入了36项研究。人工智能同时提供协助可将阅读时间减少27.20%（95%置信区间，18.22%-36.18%）。当人工智能作为第二阅读者和进行预筛查时，阅读量分别减少了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）。尽管有这些令人鼓舞的结果，但由于存在显著的异质性和研究质量参差不齐的情况，仍需谨慎对待。
