Hybrid Neuro-Symbolic Architectures for Secure Medical Decision Support Under Attack Conditions - Mateo Hawkins; Benjamin Johnston; Abhay Nair; Leif Vega (Computational Intelligence Systems, 2026)
The integration of artificial intelligence into clinical decision support systems has promised substantial improvements in diagnostic accuracy, treatment planning, and operational efficiency. However, the increasing reliance on deep learning models introduces systemic vulnerabilities, particularly under adversarial attack conditions where carefully crafted perturbations can cause catastrophic misclassifications. This paper investigates hybrid neuro-symbolic architectures as a resilient alternative for secure medical decision support. By combining the pattern recognition strengths of neural networks with the explicit reasoning capabilities of symbolic components, these architectures offer structural defenses that are not easily compromised by gradient-based or query-based adversarial manipulations. We examine the architectural trade-offs between neural and symbolic modules, the governance challenges in deploying such hybrid systems in regulated healthcare environments, and the implications for robustness, fairness, and long-term sustainability. Through a system-level analysis, we argue that neuro-symbolic integration can provide layered security, improved interpretability, and enhanced out-of-distribution detection, while also introducing new complexities in verification, maintenance, and cross-institutional scaling. The paper further discusses policy frameworks needed to support the adoption of these systems under adversarial risk models, drawing comparisons with existing standards for medical software and AI safety. A case is made for shifting from purely data-driven approaches to hybrid models that embed domain knowledge and logical constraints as first-class architectural elements. The findings suggest that hybrid neuro-symbolic architectures, when properly governed, offer a viable path toward trustworthy medical AI that remains effective even when under sustained attack.