Algorithmic Bias and Fairness in Biomedical and Health Research

Abstract

The rapid integration of artificial intelligence (AI) and machine learning (ML) into biomedical and health research has the potential to transform patient care, diagnosis, and treatment outcomes. However, as these technologies evolve, concerns surrounding algorithmic bias and fairness have emerged. In the context of healthcare, biased algorithms can exacerbate disparities in health outcomes, leading to inequality in care and undermining trust in AI-driven systems. This chapter explores the ethical implications of algorithmic bias in biomedical research, focusing on the factors contributing to bias in datasets, model design, and decision-making processes. Additionally, it examines various strategies and frameworks aimed at promoting fairness and equity in AI applications. Through a multidisciplinary lens, the chapter presents a critical analysis of how algorithmic fairness can be achieved, with particular emphasis on practical solutions and regulatory considerations to safeguard both the integrity of research and the well-being of diverse patient populations

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