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

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 co

SUBMITTER: Seyyed-Kalantari L 

PROVIDER: S-EPMC8674135 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature