Multi-Agent Collaboration with Adversarial Filtering for Reliable Medical Diagnosis Support
Keywords:
multi-agent systems, adversarial filtering, medical diagnosis support, robust AI, clinical decision support, socio-technical infrastructure, governance, algorithmic fairnessAbstract
The integration of artificial intelligence into clinical decision support systems promises to augment diagnostic accuracy and operational efficiency, yet the adoption of such systems remains constrained by concerns over reliability, robustness, and adversarial vulnerability. This paper proposes a novel architectural framework that combines multi-agent collaboration with adversarial filtering to enhance the trustworthiness of medical diagnosis support. The system comprises a suite of specialized diagnostic agents, each trained on distinct data modalities or clinical subdomains, whose outputs are aggregated through a central arbitrator. Prior to aggregation, an adversarial filtering module screens each agent contribution for potential manipulated or out-of-distribution inputs, thereby mitigating the impact of adversarial perturbations and improving overall system resilience. We examine the structural trade-offs inherent in such a distributed architecture, including the balance between agent specialization and coordination overhead, the computational cost of real-time filtering, and the implications for governance and fairness. Drawing on perspectives from large-scale socio-technical systems, we discuss deployment considerations such as interoperability with existing health information infrastructures, regulatory compliance, and sustainability of model maintenance. Ethical dimensions around algorithmic bias, accountability, and patient safety are analyzed within the context of adversarial filtering mechanisms. The paper further explores policy implications for certification and continuous monitoring of medical AI systems. By situating the proposed framework within the broader landscape of robust and trustworthy AI, we argue that multi-agent architectures enhanced with adversarial defenses offer a viable path toward reliable clinical decision support. The work contributes a systems-level blueprint for future research and practical implementation in high-stakes medical environments.
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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.
