Healthcare bias in AI: A Systematic Literature Review

The adoption of Artificial Intelligence (AI) in healthcare is transforming the field by enhancing patient care, advancing diagnostic precision, and optimizing clinical flows. Despite its promise, algorithmic bias remains a pressing challenge, raising critical concerns about fairness, equity, and the reliability of AI systems in diverse healthcare settings. This systematic literature review (SLR) investigates how bias manifests across the AI lifecycle—spanning data collection, model training, and real-world application and examines its implications for healthcare outcomes. By rigorously analyzing peer-reviewed studies based on inclusion and exclusion criteria, this review identifies the populations most impacted by bias and explores the diversity of existing mitigation strategies, fairness metrics, and ethical frameworks. Our findings reveal persistent gaps in addressing health inequities and underscore the need for targeted interventions to ensure AI systems serve as tools for equitable and ethical care. This work aims to guide future research and inform policy development, in order to prioritize both technological progress and social responsibility in healthcare AI.