Architectures of Interpretability in Deep Neural Networks for Transparent Clinical Decision Support in High-Stakes Diagnostic Environments

Abstract

The integration of deep neural networks (DNNs) in clinical decision-making systems promises unprecedented accuracy, particularly in complex, high-stakes diagnostic contexts. However, the "black-box" nature of these models poses significant risks, particularly in clinical accountability and ethical transparency. This paper explores emerging architectures and interpretability techniques tailored to clinical contexts. It categorizes state-of-the-art models, benchmarks interpretable AI frameworks, and presents a synthesis of methods validated in real-world diagnostic settings. Insights into trade-offs between transparency and performance are highlighted, along with recommendations for safe deployment.