小柯机器人

加拿大多伦多大学Laleh Seyyed-Kalantari等研究人员发现，人工智能算法对特定病人群体胸部X光片的的诊断存在偏差。相关论文于2021年12月10日在线发表在《自然—医学》杂志上。

研究人员发现了三个大型胸部X光数据集以及一个多源数据集的胸部X光病理学分类中的算法诊断不足问题。研究人员发现，使用最先进的计算机视觉技术产生的分类器一直在有选择地对服务不足的患者群体进行诊断，而且对于交叉的服务不足的亚群体，例如西班牙裔女性患者，诊断不足的比率更高。利用医学影像进行疾病诊断的人工智能系统有可能加剧现有的护理偏见，并有可能导致获得医疗的不平等，从而引起对在临床上使用这些模型的伦理问题。

据介绍，人工智能（AI）系统在医学成像应用中越来越多地达到专家级的表现。然而，人们越来越担心这种人工智能系统可能反映和放大人类的偏见，并降低其在历史上服务不足的人群中的表现质量，如女性患者、黑人患者或社会经济地位低下的患者。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某种疾病的个人标记为健康，可能会延迟获得护理。

附：英文原文

Title: Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations

Author: Seyyed-Kalantari, Laleh, Zhang, Haoran, McDermott, Matthew B. A., Chen, Irene Y., Ghassemi, Marzyeh

Issue&Volume: 2021-12-10

Abstract: 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.

DOI: 10.1038/s41591-021-01595-0

Source: https://www.nature.com/articles/s41591-021-01595-0

Nature Medicine：《自然—医学》，创刊于1995年。隶属于施普林格·自然出版集团，最新IF：87.241
官方网址：https://www.nature.com/nm/
投稿链接：https://mts-nmed.nature.com/cgi-bin/main.plex