Enhancing Clinical Decision Support with Explainable Deep Learning and Fuzzy Reasoning
Keywords:
Clinical decision support, Deep learning, Deep Neural Networks (DNNs), Explainable AI, Fuzzy logic, Health monitoring, Medical data analysis, Model interpretability, Transparency, Uncertainty handlingAbstract
The swift integration of deep learning into healthcare has contributed to unprecedented advances in diagnostics, health monitoring, and clinical decision support. Still, the black-box nature of Deep Neural Networks (DNNs) poses a significant hurdle to their greater use in clinical environments, where transparency and trust are paramount. This survey introduces a thorough overview of explainable deep learning methods specifically designed for health monitoring applications. We systematically classify and critically review intrinsic interpretability techniques, visualization-based explanations, and model distillation techniques, emphasizing their strengths and weaknesses in the context of medicine. Acknowledging the distinctive challenges presented by healthcare data, e.g., uncertainty, ambiguity, and the necessity of subtle decision-making, we introduce fuzzy logic as a new and complementary model interpretability framework. Fuzzy logic offers a systematic framework to represent uncertainty and detail intricate decision-making processes, thus being an enriching complement to conventional explainability techniques. By combining fuzzy reasoning and deep learning, this work introduces a new vision to close the gap between black-box models and clinical usability. Our survey is intended to stimulate a better understanding of explainable AI for health monitoring and encourage the creation of transparent, dependable, and explainable AI systems that can fit into clinical pipelines and enhance patient care.
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