Continual Learning for Adversarially Robust Medical AI Agents in Evolving Disease Landscapes
Keywords:
continual learning, adversarial robustness, medical artificial intelligence, disease evolution, system architecture, clinical decision support, model governance, fairnessAbstract
The deployment of artificial intelligence agents in clinical settings demands not only high diagnostic accuracy but also sustained robustness against adversarial perturbations and the capacity to adapt to constantly shifting disease landscapes. Medical AI systems, from diagnostic imaging classifiers to clinical decision support tools, are currently trained on static datasets that quickly become outdated as pathogens mutate, treatment protocols change, and population demographics evolve. This paper presents a comprehensive system-level analysis of continual learning frameworks designed to maintain adversarial robustness in medical AI agents operating under real-world constraints. We examine architectural trade-offs between plasticity for new knowledge acquisition and stability for retaining previously learned representations, particularly when those representations are vulnerable to adversarial attacks that can degrade patient safety. The discussion spans infrastructure requirements for online model updates, governance mechanisms for certifying deployed models after each retraining cycle, and the ethical implications of adaptive systems that must balance fairness across subpopulations while defending against malicious inputs. Through cross-domain comparisons with autonomous driving and cybersecurity, we illustrate how medical AI faces unique challenges due to high stakes, heterogeneous data distributions, and regulatory oversight. We propose a conceptual framework that integrates continual learning with adversarial training, uncertainty quantification, and human-in-the-loop validation. The paper further addresses sustainability concerns, including computational cost, energy consumption, and the need for decentralized data governance in federated learning topologies. Finally, we outline policy recommendations for regulatory bodies to ensure that adaptive medical AI agents remain both safe and equitable as disease landscapes continue to evolve.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
