Enhancing Clinical Decision Support: A Hybrid Framework Integrating Markov Decision Processes and Explainable Neurosymbolic Reasoning

Abstract

The integration of Artificial Intelligence (AI) into healthcare promises to revolutionize diagnostic accuracy and treatment optimization. However, the deployment of these systems faces significant hurdles related to trust, explainability, and the ability to reason through complex, sequential clinical scenarios. Current "black-box" deep learning models often fail to provide the transparency required for medical and legal accountability, while traditional rulebased systems lack the flexibility to learn from vast biomedical datasets. This paper proposes a hybrid framework that combines Markov Decision Processes (MDPs) for temporal treatment planning with Neurosymbolic AI to integrate logical reasoning with data-driven learning. By prioritizing explainability and the rigorous management of uncertainty, this approach aims to support clinicians in making optimal, evidence-based decisions. We discuss the theoretical underpinnings of this method, analyse its relation to existing literature, and outline a robust evaluation strategy to measure both performance and clinical helpfulness.

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