Explainable AI for Healthcare Diagnostics

The integration of Artificial Intelligence (AI) in healthcare diagnostics has revolutionized the field by enabling faster, more accurate, and data-driven decision-making. However, the complexity and opacity of many AI models, particularly deep learning algorithms, pose significant challenges to trust, accountability, and clinical adoption. Explainable AI (XAI) emerges as a critical solution to bridge this gap, offering transparency and interpretability to AI-driven healthcare systems. This paper explores the significance of XAI in healthcare diagnostics, examining various methods such as SHAP, LIME, and attention mechanisms that enhance model interpretability without compromising performance. We analyze real-world case studies and develop a working diagnostic model with explainable outputs, demonstrating how XAI can improve clinician trust, patient safety, and regulatory compliance. The study concludes with insights into the current challenges and future directions of implementing XAI in clinical settings.