Understanding AI Bias in Clinical Practice

This review explores how bias in AI models used for clinical decision support can lead to patient harm, distinguishing between inherent bias in underlying datasets and labeling bias from error‑prone endpoints. It recommends greater transparency about training data and model limitations, rigorous validation across diverse patient populations, and prospective studies of clinical outcomes to ensure that AI‑driven decisions do not systematically disadvantage specific groups.