Ethical AI in Medicine: Bias Mitigation in Clinical Decision Support Systems

As artificial intelligence becomes more integrated into clinical decision support (CDS) systems, the risk of perpetuating or even amplifying existing health disparities becomes a critical concern. Algorithmic bias, often stemming from unrepresentative or historically skewed training data, can lead to inequitable care for minority populations. This paper provides a comprehensive review of state-of-the-art methodologies for identifying and mitigating bias in medical AI. We analyze pre-processing techniques (e.g., data augmentation, re-sampling), in-processing methods (e.g., adversarial debiasing, fairness constraints), and post-processing adjustments (e.g., fairness-aware calibration). We then propose a novel, hybrid framework for developing fair and equitable CDS tools. This framework emphasizes transparency, model interpretability, and continuous auditing. We argue that ensuring ethical AI is not merely a technical challenge but a socio-technical one, requiring collaboration between data scientists, clinicians, ethicists, and regulatory bodies to build systems that all patients can trust.