Algorithmic Bias in Medical Imaging

This review examines algorithmic bias in medical imaging. The organizing question is how dataset construction, acquisition, labeling, model design, and workflow placement create or mitigate unequal error. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to using overall accuracy to conceal subgroup harm and access disparities. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in clinical imaging and computational diagnostics.

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