Search⌘ K
AI Features

Case Study: Chest X-rays

Understand the impact of bias in AI models used for chest X-ray diagnostics, focusing on underdiagnosis among minority and economically disadvantaged groups. Learn methods to identify biases in data and models, and explore practical mitigation techniques including data observability, model explainability, debiasing, and ongoing monitoring for fairer medical AI solutions.

In this case study, we’ll be diving into a particular case of model bias in the medical sector. In December 2021, a paperSeyyed-Kalantari, L., et al. “Underdiagnosis Bias of Artificial Intelligence Algorithms Applied to Chest Radiographs in Under-served Patient Populations.” 2021. Pacific Symposium on Biocomputing, vol. 26, pp. 232-243. PMID: 33691020. published in Nature revealed that algorithms meant to diagnose patients based on their chest radiographs were often biased in a way that underdiagnosed underserved populations.

Background

Chest x-rays are one particularly useful area for ML and AI models to speed up diagnoses. Chest x-rays are easily obtainable data sources that have clear targets (diagnosis or no ...