本文由小咖机器人翻译整理

期刊来源：Nat Med

原文链接：https://doi.org/10.1038/s41591-023-02728-3

摘要内容如下：

尽管用于基于图像的医学诊断的深度学习系统的进步证明了其增强临床决策的潜力，但医生-机器合作的有效性仍是一个悬而未决的问题，部分原因是医生和算法都容易受到系统错误的影响，特别是对于未被充分代表的人群的诊断。在这里，我们展示了一项大规模数字实验的结果，该实验涉及来自39个国家的委员会认证的皮肤科医生（n=389）和初级保健医生（n=459），以评估医生在存储转发远程皮肤病学模拟中提交的诊断的准确性。在这个实验中，医生们被提供了364张涵盖46种皮肤病的图像，并被要求提交最多四种鉴别诊断。专家和全科医生的诊断准确率分别为38%和19%，但专家和全科医生对深色皮肤图像的诊断准确率比浅色皮肤图像低4个百分点。Fair深度学习系统决策支持将专家和通才的诊断准确率提高了33%以上，但加剧了通才在不同肤色之间的诊断准确率差距。这些结果表明，设计良好的医生-机器伙伴关系可以提高医生的诊断准确性，说明提高整体诊断准确性的成功并不一定能解决偏差。

英文原文如下：

Abstracts

Although advances in deep learning systems for image-based medical diagnosis demonstrate their potential to augment clinical decision-making, the effectiveness of physician-machine partnerships remains an open question, in part because physicians and algorithms are both susceptible to systematic errors, especially for diagnosis of underrepresented populations. Here we present results from a large-scale digital experiment involving board-certified dermatologists (n = 389) and primary-care physicians (n = 459) from 39 countries to evaluate the accuracy of diagnoses submitted by physicians in a store-and-forward teledermatology simulation. In this experiment, physicians were presented with 364 images spanning 46 skin diseases and asked to submit up to four differential diagnoses. Specialists and generalists achieved diagnostic accuracies of 38% and 19%, respectively, but both specialists and generalists were four percentage points less accurate for the diagnosis of images of dark skin as compared to light skin. Fair deep learning system decision support improved the diagnostic accuracy of both specialists and generalists by more than 33%, but exacerbated the gap in the diagnostic accuracy of generalists across skin tones. These results demonstrate that well-designed physician-machine partnerships can enhance the diagnostic accuracy of physicians, illustrating that success in improving overall diagnostic accuracy does not necessarily address bias.

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