Neuro-Symbolic AI Framework for Explainable and Reliable Clinical Decision Support

Abstract

The adoption of artificial intelligence in healthcare is often limited by the lack of interpretability and reliability in complex models. This paper presents a Neuro-Symbolic AI Framework for Explainable and Reliable Clinical Decision Support, combining the strengths of deep learning and symbolic reasoning. The proposed system integrates neural networks for pattern recognition with symbolic knowledge representations such as medical ontologies and rule-based systems to enhance interpretability and logical reasoning. This hybrid approach enables the system to not only learn from large-scale healthcare data but also provide human-understandable explanations for its predictions. The framework incorporates knowledge graphs to represent relationships among clinical entities and uses reasoning mechanisms to ensure consistency and accuracy in decision-making. Additionally, uncertainty handling and validation modules are included to improve system reliability in critical healthcare scenarios. Experimental results demonstrate that the proposed approach achieves competitive predictive performance while significantly improving explainability compared to purely data-driven models. Integration with prior research in healthcare analytics further strengthens system robustness and generalization. The study highlights the potential of neuro-symbolic AI in bridging the gap between data-driven intelligence and human-centric clinical reasoning.