皮肤病影响全球三分之一的人口，造成重大的医疗负担。深度学习可以通过神经网络处理皮肤图像进行预测来优化医疗保健工作流程。深度学习研究的一个重点是通过皮肤病变分类来检测癌症，但这可能无法转化为更广泛的超过 2000 种其他皮肤病。我们检索了将深度学习应用于皮肤图像的研究，排除良性/恶性病变（1/1/2000-23/6/2022，PROSPERO CRD42022309935）。主要结果是深度学习算法在疾病诊断或严重程度评估中的准确性。我们修改了 QUADAS-2 以进行质量评估。在确定的 13,857 篇参考文献中，纳入了 64 篇。研究最多的疾病是痤疮、牛皮癣、湿疹、酒渣鼻、白癜风、荨麻疹。深度学习算法在诊断这些情况时具有高度特异性和可变敏感性。算法诊断痤疮（中位数 94%，IQR 86-98；n = 11）、红斑痤疮（94%，90-97；n = 4）、湿疹（93%，90-99；n = 9）和牛皮癣的准确性（89%，78-92；n = 8）很高。银屑病（范围 93-100%，n = 2）、湿疹（88%，n = 1）和痤疮（67-86%，n = 4）的严重程度分级准确度最高。然而，59 项（92%）研究存在高偏倚风险判断，62 项（97%）研究存在高度适用性问题。只有 12 人 (19%) 报告了参与者的种族/皮肤类型。二十四名 (37.5%) 在独立数据集、临床环境或前瞻性中评估了该算法。这些数据表明深度学习图像分析在诊断和监测常见皮肤病方面的潜力。目前的研究存在重要的方法/报告局限性。具有外部验证/测试的真实世界、预期获取的图像数据集将推动深度学习超越当前实验阶段，转向临床有用的工具，以减轻皮肤病日益严重的健康和成本影响。© 2023。Springer Nature Limited。

Skin diseases affect one-third of the global population, posing a major healthcare burden. Deep learning may optimise healthcare workflows through processing skin images via neural networks to make predictions. A focus of deep learning research is skin lesion triage to detect cancer, but this may not translate to the wider scope of >2000 other skin diseases. We searched for studies applying deep learning to skin images, excluding benign/malignant lesions (1/1/2000-23/6/2022, PROSPERO CRD42022309935). The primary outcome was accuracy of deep learning algorithms in disease diagnosis or severity assessment. We modified QUADAS-2 for quality assessment. Of 13,857 references identified, 64 were included. The most studied diseases were acne, psoriasis, eczema, rosacea, vitiligo, urticaria. Deep learning algorithms had high specificity and variable sensitivity in diagnosing these conditions. Accuracy of algorithms in diagnosing acne (median 94%, IQR 86-98; n = 11), rosacea (94%, 90-97; n = 4), eczema (93%, 90-99; n = 9) and psoriasis (89%, 78-92; n = 8) was high. Accuracy for grading severity was highest for psoriasis (range 93-100%, n = 2), eczema (88%, n = 1), and acne (67-86%, n = 4). However, 59 (92%) studies had high risk-of-bias judgements and 62 (97%) had high-level applicability concerns. Only 12 (19%) reported participant ethnicity/skin type. Twenty-four (37.5%) evaluated the algorithm in an independent dataset, clinical setting or prospectively. These data indicate potential of deep learning image analysis in diagnosing and monitoring common skin diseases. Current research has important methodological/reporting limitations. Real-world, prospectively-acquired image datasets with external validation/testing will advance deep learning beyond the current experimental phase towards clinically-useful tools to mitigate rising health and cost impacts of skin disease.© 2023. Springer Nature Limited.