Blockchain-Enabled Secure Collaboration of Multi-Agent Large Language Model Systems for Clinical Decision Support - Maxime Fdwards; Tobias Simpson (International Journal of Clinical and Translational Medicine, 2026)
The integration of large language models into clinical decision support introduces transformative potential for diagnostic reasoning, treatment planning, and patient communication. However, real-world deployment of such models within high-stakes medical environments demands robust mechanisms for inter-model coordination, auditability, privacy preservation, and adversarial resilience. This paper proposes a system-level architecture in which multiple specialized large language model agents, each acting as a distinct clinical reasoning entity, collaborate through a blockchain-mediated coordination protocol. The blockchain fabric serves not merely as a tamper-evident log but as a decentralized governance layer that enforces access policies, tracks provenance of medical inferences, and enables cryptographically assured consensus among autonomous agents. We examine the structural trade-offs inherent in coupling multi-agent language intelligence with distributed ledger technology, including latency, throughput, and the tension between transparent audit trails and patient data confidentiality. A central analytical focus concerns the adversarial robustness of language model agents operating in clinical contexts, where manipulated inputs or coordinated poisoning attacks could subvert collective decision processes. Through a cross-domain lens that integrates insights from federated learning, decentralized identity management, and distributed systems governance, the paper discusses architectural principles for sustainable, fair, and regulation-compliant multi-agent clinical intelligence. The analysis extends to policy implications, infrastructure requirements, and the long-term sustainability of such hybrid computational ecosystems. Ultimately, the paper argues that blockchain-enabled secure collaboration offers a pathway toward verifiable, resilient, and ethically governed clinical AI, while also delineating the substantial engineering and institutional challenges that must be overcome.