Addressing Bias in AI Algorithms for Health Applications

Artificial Intelligence (AI) has transformed healthcare by enhancing diagnostic accuracy, treatment personalization, and health service efficiency. However, mounting evidence reveals that AI systems can perpetuate or even amplify existing disparities related to race, gender, socioeconomic status, and geographic location. Biases often originate from imbalanced training datasets, flawed algorithm design, and unequal data collection practices. These biases have led to misdiagnoses, unequal resource allocation, and inadequate treatment recommendations, disproportionately affecting marginalized communities. This review explores the roots of algorithmic bias in healthcare AI, analyzing real-world examples such as COVID-19 triage systems and diagnostic tools that underperform in minority populations. It also examines mitigation strategies, including bias-aware data collection, algorithm design techniques, regulatory frameworks, and stakeholder engagement. Successful case studies and future research directions are presented, emphasizing fairness, transparency, and trust in computational medicine. Establishing robust, bias-resilient AI frameworks is critical to achieving equitable health outcomes and reinforcing the ethical foundations of digital health. Keywords: AI bias, health equity, algorithmic fairness, medical AI, healthcare disparities, machine learning, ethical AI, computational medicine.