Challenges in Health Equity

Abstract

The rapid adoption of Artificial Intelligence (AI) in healthcare has revolutionized diagnostics, treatment planning, and patient care, but it has also raised critical ethical concerns, particularly regarding health equity. Algorithmic biases, often stemming from imbalanced datasets, result in unequal performance, with marginalized communities experiencing reduced accuracy and higher misdiagnosis rates. This chapter aims to investigate the impact of such biases and propose strategies to mitigate them. Using a structured methodology, the chapter analysed AI models in diagnostic and treatment prediction tasks with diverse datasets, implemented data augmentation, re-sampling, and fairness-aware algorithms, and evaluated improvements using equity metrics. However, applying these mitigation strategies reduced biases and improved equity metrics. Finally, the chapter underscores the need for ethical frameworks and inclusive data practices to balance innovation with fairness and ensure equitable healthcare outcomes for all.

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