ETHICAL AND RACIAL BIAS IN ARTIFICIAL INTELLIGENCE IN HEALTHCARE: A SURVEY OF CHALLENGES, CONSEQUENCES, AND MITIGATION STRATEGIES

Abstract

Artificial intelligence is changing the way we approach healthcare. It's helping doctors diagnose illnesses faster, tailor treatments to individual patients, and streamline daily operations. But as these technologies become more common, we need to be mindful of the biases they might carry, especially those that could harm communities already facing disparities in care. This paper brings together findings from key studies to explore how bias enters medical AI systems, how it affects real-world outcomes like misdiagnoses or unequal access to treatment, and how it can create mistrust in AI-driven care. These issues often stem from the data we use, the design choices we make, and the clinical priorities we set. Addressing them isn't just about improving accuracy; it's about ensuring fairness, accountability, and respect for patient autonomy. The paper also discusses practical steps to reduce these harms, such as creating more inclusive datasets, designing algorithms with fairness in mind, and promoting transparency through open science. It offers guidance for developing AI tools in medicine that are not only effective but also equitable and trustworthy for everyone.