The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1/1/2017 to 11/8/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6–80.1) and specificity was 81.5% (95% CI 73.9–87.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4–86.5) and specificity was 86.1% (95% CI 79.2–90.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.

基于人工智能（AI）的皮肤癌诊断工具的开发正在迅速增长，并且可能很快就会广泛应用于临床。尽管这些算法的性能在理论上很有希望，但关于人工智能辅助对人类诊断决策的影响的证据有限。因此，本次系统评价和荟萃分析的目的是研究人工智能辅助对皮肤癌诊断准确性的影响。我们检索了 PubMed、Embase、IEE Xplore、Scopus 和会议记录中 2017 年 1 月 1 日至 2022 年 8 月 11 日的文章。我们纳入的研究比较了临床医生在有或没有基于深度学习的人工智能辅助的情况下诊断至少一种皮肤癌的表现。使用双变量随机效应模型计算了有人工智能辅助与无人工智能辅助时诊断准确性的敏感性和特异性的汇总估计。我们确定了 2983 项研究，其中 10 项适合进行荟萃分析。对于没有 AI 辅助的临床医生，汇总敏感性为 74.8% (95% CI 68.6–80.1)，特异性为 81.5% (95% CI 73.9–87.3)。对于人工智能辅助的临床医生，总体敏感性为 81.1% (95% CI 74.4–86.5)，特异性为 86.1% (95% CI 79.2–90.9)。在亚组分析中，人工智能使所有经验水平的医疗专业人员受益，其中非皮肤科医生的进步最大。没有发现发表偏倚，敏感性分析表明研究结果是稳健的。临床医生手中的人工智能有可能提高皮肤癌诊断的准确性。鉴于大多数研究都是在实验环境中进行的，我们鼓励未来的研究进一步研究现实生活中的这些潜在好处。