Explainable Neuro-Symbolic Medical Agent Systems with Adversarial Resilience for Evidence-Based Clinical Decision Intelligence - Ashwin L. Sinha; Pranav Mittal; Prakash Maidu; Buraj Kulkarni (International Journal of Clinical and Translational Medicine, 2026)
The integration of neural learning and symbolic reasoning in medical agent systems offers transformative potential for evidence-based clinical decision intelligence, yet introduces formidable challenges in explainability, adversarial robustness, and safe deployment. This paper provides a system-level analysis of neuro-symbolic architectures designed for clinical decision support, examining structural trade-offs between predictive performance and interpretability, and between robustness and real-time clinical responsiveness. We explore how the coupling of large language models with clinical knowledge graphs, ontologies, and logical inference can yield transparent reasoning chains suitable for clinical auditing, while identifying vulnerabilities that adversarial perturbations can exploit to distort evidence-dependent recommendations. Defense strategies that span certified robustness techniques, symbolic sanity checks, and input sanitation layers are evaluated in terms of their effect on diagnostic accuracy and workflow latency. A governance framework is proposed that integrates fairness audits, continuous monitoring, and regulatory alignment with evolving standards for software as a medical device. The discussion extends to infrastructure scalability, energy sustainability, and the policy implications of embedding such agents into hospital information ecosystems. By synthesizing cross-domain insights, the paper identifies tensions between model expressiveness and explanation fidelity, and between adversarial resilience and computational overhead, contributing a holistic design perspective for trustworthy clinical agents.