Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations.
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations.
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DOI：
10.1038/s41591-021-01595-0
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发表时间：
2021-12
期刊：
影响因子：
82.9
通讯作者：
Ghassemi M
中科院分区：
文献类型：
--
作者：
Seyyed-Kalantari L;Zhang H;McDermott MBA;Chen IY;Ghassemi M
人工智能（AI）系统在医学成像应用中越来越多地实现了专家级性能。然而，人们越来越担心，这种人工智能系统可能会反映和放大人类偏见，并降低其在女性患者、黑人患者或社会经济地位低的患者等历史上服务不足的人群中的表现质量。在诊断不足的情况下，这种偏见尤其令人不安，人工智能算法会不准确地将患有疾病的个体标记为健康，可能会延迟获得护理。在这里，我们检查了三个大型胸部X射线数据集以及一个多源数据集的胸部X射线病理分类中的算法诊断不足。我们发现，使用最先进的计算机视觉技术产生的分类器始终和选择性地诊断不足的患者人群，并且诊断不足率对于交叉服务不足的亚群较高，例如，西班牙裔女性患者。使用医疗成像进行疾病诊断的AI系统的部署可能会加剧现有的医疗偏见，并可能导致不平等的医疗机会，从而引发对临床使用这些模型的伦理问题。使用胸部X光片训练的人工智能算法一直低估了历史上服务不足的患者群体中的肺部异常或疾病，引发了对此类算法临床使用的伦理担忧。
Artificial intelligence (AI) systems have increasingly achieved expert-level performance in medical imaging applications. However, there is growing concern that such AI systems may reflect and amplify human bias, and reduce the quality of their performance in historically under-served populations such as female patients, Black patients, or patients of low socioeconomic status. Such biases are especially troubling in the context of underdiagnosis, whereby the AI algorithm would inaccurately label an individual with a disease as healthy, potentially delaying access to care. Here, we examine algorithmic underdiagnosis in chest X-ray pathology classification across three large chest X-ray datasets, as well as one multi-source dataset. We find that classifiers produced using state-of-the-art computer vision techniques consistently and selectively underdiagnosed under-served patient populations and that the underdiagnosis rate was higher for intersectional under-served subpopulations, for example, Hispanic female patients. Deployment of AI systems using medical imaging for disease diagnosis with such biases risks exacerbation of existing care biases and can potentially lead to unequal access to medical treatment, thereby raising ethical concerns for the use of these models in the clinic. Artificial intelligence algorithms trained using chest X-rays consistently underdiagnose pulmonary abnormalities or diseases in historically under-served patient populations, raising ethical concerns about the clinical use of such algorithms.
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DOI：
10.1145/3368555.3384468
发表时间：
2020-04
期刊：
Proceedings of the ACM Conference on Health, Inference, and Learning
影响因子：
--
作者：
Oakden-Rayner L;Dunnmon J;Carneiro G;Ré C
通讯作者：
Ré C
影响因子：
0.5
作者：
Bhatt M;Kant S;Bhaskar R
通讯作者：
Bhaskar R
DOI：
10.1056/nejmp1714229
发表时间：
2018-03-15
期刊：
The New England journal of medicine
影响因子：
--
作者：
Char DS;Shah NH;Magnus D
通讯作者：
Magnus D
DOI：
10.15326/jcopdf.5.3.2017.0145
发表时间：
2018-07-01
影响因子：
2.4
作者：
Mamary, A. James;Stewart, Jeffery I.;Criner, Gerard J.
通讯作者：
Criner, Gerard J.
影响因子：
37.8
作者：
Goldberger, AL;Amaral, LAN;Stanley, HE
通讯作者：
Stanley, HE