Trustworthy Artificial Intelligence in Clinical Decision Support Systems: A Framework for Explainability and Fairness

The rapid integration of Artificial Intelligence (AI) into healthcare promises to revolutionize diagnostics and patient care. However, the deployment of "Black Box" Deep Learning models raises critical concerns regarding trust, accountability, and safety. This paper explores the concept of Trustworthy AI within the context of Clinical Decision Support Systems (CDSS). We analyze the two primary pillars of trustworthiness: Explainable AI (XAI) and Algorithmic Fairness. By reviewing recent architectures and regulatory frameworks such as the EU AI Act, we propose a comprehensive evaluation metric for medical AI models that balances high accuracy with interpretability and non-discrimination. Our findings suggest that while trade-offs between performance and explainability exist, hybrid models incorporating "human-in-the-loop" mechanisms are essential for the ethical deployment of AI in high-stakes environments.

Keywords:

Trustworthy AI , Explainable AI (XAI) , Algorithmic Fairness , Healthcare , Deep Learning