Source: Nature

AI technologies are increasingly used in healthcare to improve diagnosis, treatment, and administrative efficiency, yet they often inherit biases from the data they are trained on, leading to disparities in patient care. This article explores how biased algorithms can perpetuate systemic inequalities, particularly for marginalized groups, by producing inaccurate diagnoses or excluding specific populations from clinical trials. The ethical implications of these biases are significant, raising concerns about trust, accountability, and fairness in AI-driven healthcare systems. Researchers and policymakers are working to mitigate these issues by advocating for more diverse datasets, transparent algorithms, and stricter ethical guidelines. The article underscores the importance of collaboration between technologists, healthcare professionals, and regulators to ensure that AI systems are designed to reduce, rather than exacerbate, healthcare inequalities.