NeuroSymbolic Clinical Decision Agents with Fast Reactive Policies and Slow Diagnostic Reasoning - Lucas Bailey; Kiran Mishra (Computational Intelligence Systems, 2026)
The integration of artificial intelligence into clinical decision-making has historically oscillated between purely data-driven deep learning approaches and rule-based symbolic reasoning. Each paradigm offers distinct advantages: neural networks excel at pattern recognition from raw patient data, while symbolic systems provide transparent, logical inference. This paper proposes a hybrid neurosymbolic architecture for clinical decision agents that explicitly separates fast, reactive policies from slow, deliberative diagnostic reasoning, inspired by dual-process cognitive theories. The fast component learns low-latency, high-frequency responses for routine or time-critical tasks such as triage alerts, medication dosing adjustments, and flagging abnormal vital signs. The slow component engages in structured, multi-step diagnostic reasoning using knowledge graphs, patient history, and clinical guidelines to generate explainable differential diagnoses and treatment plans. We examine the structural trade-offs between these two modes, including latency versus accuracy, transparency versus performance, and adaptability versus stability. The paper further explores system-level considerations for deployment in hospital infrastructures, including governance frameworks, data privacy, regulatory compliance, and the need for continuous validation across diverse populations. Challenges of robustness against distributional shifts, fairness across demographic groups, and long-term sustainability of such hybrid systems are analyzed through case illustrations from emergency medicine, chronic disease management, and telemedicine. We conclude with forward-looking perspectives on how neurosymbolic clinical agents can be designed to augment rather than replace human clinicians, ensuring that fast and slow reasoning complement each other within a human-in-the-loop framework. The proposed architecture offers a path toward accountable, adaptable, and trustworthy AI in healthcare.