The age of deep learning has brought high-performing diagnostic models for a variety of healthcare conditions. Deep neural networks can, in principle, approximate any function. However, this power can be considered both a gift and a curse, as the propensity towards overfitting is magnified when the input data are heterogeneous and high dimensional coupled with an output class which is highly nonlinear. This issue can especially plague diagnostic systems which predict behavioral and psychiatric conditions that are diagnosed with subjective criteria. An emerging solution to this issue is crowdsourcing, where crowd workers are paid to annotate complex behavioral features in return for monetary compensation or a gamified experience. These labels can then be used to derive a diagnosis, either directly or by using the labels as inputs to a diagnostic machine learning model. Here, I describe existing work in this field. I then discuss ongoing challenges and opportunities with crowd-powered diagnostic systems. With the correct considerations, the addition of crowdsourcing into machine learning workflows for prediction of complex and nuanced health conditions can rapidly accelerate screening, diagnostics, and ultimately access to care.

翻译：深度学习时代带来了针对多种健康状况的高性能诊断模型。深度神经网络原则上可以逼近任意函数。然而，这种能力既是馈赠也是诅咒，因为当输入数据具有异质性和高维性且输出类别高度非线性时，过拟合倾向会被放大。这一问题尤其会困扰那些预测依据主观标准诊断的行为与精神类疾病的诊断系统。针对这一问题的新兴解决方案是众包，即通过支付报酬或提供游戏化体验来让众包工作者标注复杂的行为特征。这些标注可用于直接推导诊断结论，或作为诊断机器学习模型的输入。本文概述了该领域的现有研究，随后探讨了众包驱动诊断系统面临的持续挑战与机遇。若加以正确考量，将众包融入针对复杂细微健康状况的机器学习预测流程，可显著加速筛查与诊断进程，最终改善患者获得医疗服务的可及性。