Unmasking the algorithm: a review of algorithmic bias and fairness in medical AI and image processing
TL;DR
The taxonomy of ethical concerns is categorized—including algorithmic fairness, privacy, transparency, and clinical accountability—and the workflow of bias from unrepresentative training datasets through flawed objective functions to biased clinical deployment is traced.
Abstract
The integration of Generative Artificial Intelligence (AI) into medical image processing has substantial potential for transforming diagnostic workflows, accelerating image reconstruction, and improving clinical decision-making. However, this technological shift brings significant ethical challenges, particularly concerning algorithmic bias and fairness. This paper reviews the ethical issues surrounding AI-generated content in medical imaging, focusing on how biases are introduced and amplified, and how they impact patient care across diverse demographic groups. We categorize the taxonomy of ethical concerns—including algorithmic fairness, privacy, transparency, and clinical accountability—and trace the workflow of bias from unrepresentative training datasets through flawed objective functions to biased clinical deployment. By analyzing the vulnerabilities of generative models, such as Generative Adversarial Networks (GANs) and diffusion models, we examine the phenomenon of underdiagnosis bias and the magnification of historical disparities. We also discuss mitigation strategies, including data-centric approaches to ensure representative sampling and model-centric techniques such as adversarial debiasing. Ensuring fairness and equity in medical AI is necessary for its safe and effective clinical adoption.