Explainable AI in Healthcare: Enhancing Transparency in Medical Diagnosis Systems

Abstract

Artificial Intelligence (AI) is revolutionizing healthcare by enabling automated medical
diagnosis, predictive analytics, and personalized treatment recommendations. However, the lack
of transparency and interpretability in AI-driven medical systems raises ethical, legal, and
clinical concerns. Explainable AI (XAI) aims to bridge this gap by making AI decisions
transparent, interpretable, and trustworthy for healthcare professionals and patients.
This research explores how XAI enhances trust, accountability, and clinical decision-making by
improving the interpretability of medical AI models. A novel framework combining deep
learning with interpretable techniques like SHAP (SHapley Additive Explanations), LIME
(Local Interpretable Model-agnostic Explanations), and attention mechanisms is proposed for
medical diagnosis. Experimental results demonstrate that XAI enhances physician trust,
improves diagnostic accuracy, and facilitates regulatory compliance in healthcare AI
applications. The study also evaluates the challenges of implementing explainability techniques
and proposes future research directions to improve AI adoption in clinical settings.