Architectures of Interpretability in Deep Neural Networks for Transparent Clinical Decision Support in High-Stakes Diagnostic Environments
Keywords:
Deep Neural Networks, Interpretability, Clinical Decision Support Systems, Transparent AI, XAI, Medical Diagnosis, Black-box Models, High-Stakes AIAbstract
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.
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Copyright (c) 2022 Jakes Willam Frose, (Author)
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