Advancing Explainable Artificial Intelligence for Clinical Decision Support: Techniques, Challenges, and Evaluation Frameworks in High-Stakes Medical Environments

As artificial intelligence (AI) continues to transform the landscape of healthcare, the integration of Explainable Artificial Intelligence (XAI) into clinical decision support systems (CDSS) has emerged as a critical necessity. This chapter explores the vital role of XAI in enhancing the interpretability and transparency of AI-driven medical applications, particularly in high-stakes environments where decisions can profoundly impact patient outcomes. We begin by defining XAI and its significance in fostering trust among healthcare professionals and patients alike, emphasizing the ethical imperatives of accountability and safety in clinical settings. We examine a range of techniques for achieving explainability, categorizing them into model-agnostic methods, model-specific approaches, visualization techniques, and case-based reasoning. Each category is discussed with respect to its applicability and effectiveness in conveying understandable insights to clinicians. Additionally, we address the multifaceted challenges associated with implementing XAI, including the complexity of medical data, the inherent trade-offs between model accuracy and interpretability, and the resistance from healthcare professionals accustomed to traditional decision-making processes. To ensure the successful deployment of XAI in CDSS, we propose comprehensive evaluation frameworks that assess the clarity, consistency, and actionability of explanations provided by AI systems. User-centered evaluation methods, such as surveys and usability testing, are discussed as essential tools for gathering feedback from healthcare practitioners, thereby enhancing the integration of XAI into clinical workflows. Through case studies of successful implementations, we highlight the practical benefits of XAI in predictive analytics and treatment recommendations, showcasing how explainability enhances clinical outcomes and decision-making processes. The chapter concludes by identifying future directions for research and development, including advancements in XAI techniques, the incorporation of XAI with emerging technologies, and collaborative efforts among stakeholders to promote the adoption of explainable systems in healthcare. Ultimately, this chapter underscores the imperative of advancing explainable AI in clinical decision support, advocating for a balanced approach that prioritizes both technological innovation and the critical human elements of trust, transparency, and ethical responsibility in patient care.