Exploring Explainable Deep Learning Models for Healthcare Applications

Abstract

In recent years, deep learning models have revolutionized healthcare by offering significant advancements in diagnostic accuracy, predictive analytics, and personalized treatment recommendations. However, the inherent complexity and black box nature of these models have raised concerns regarding their interpretability, especially in critical domains like healthcare where transparency is vital. This paper explores the integration of explainability into deep learning models for healthcare applications, examining methods such as attention mechanisms, Layer-wise Relevance Propagation (LRP), and Grad-CAM (Gradient-weighted Class Activation Mapping). The study delves into the importance of explainable models to ensure trust, transparency, and accountability, particularly in patient-centered care, where clinicians require clear reasoning behind model decisions. Furthermore, the paper discusses challenges in implementing explainable models and highlights future directions for balancing accuracy and interpretability in healthcare-focused deep learning systems.