Acquisition parameters influence AI recognition of race in chest x-rays and mitigating these factors reduces underdiagnosis bias.

ABSTRACT: A core motivation for the use of artificial intelligence (AI) in medicine is to reduce existing healthcare disparities. Yet, recent studies have demonstrated two distinct findings: (1) AI models can show performance biases in underserved populations, and (2) these same models can be directly trained to recognize patient demographics, such as predicting self-reported race from medical images alone. Here, we investigate how these findings may be related, with an end goal of reducing a previously identified underdiagnosis bias. Using two popular chest x-ray datasets, we first demonstrate that technical parameters related to image acquisition and processing influence AI models trained to predict patient race, where these results partly reflect underlying biases in the original clinical dataset

SUBMITTER: Lotter W 

PROVIDER: S-EPMC11358468 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature