Privacy‐preserving multi‐agent systems for human‐centred inclusive healthcare AI

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Abstract
Healthcare environments present uniquely demanding constraints for the deployment of artificial intelligence (AI). Clinical decisions of significant consequence are rarely the product of a single isolated computation; rather, safe and effective patient care relies on the continuous collaboration of distributed entities, including clinicians, hospital networks, laboratory systems, monitoring devices, and patients themselves. As generative models and large language models are increasingly deployed in clinical settings, the transition from monolithic AI models to Multi‐Agent Systems (MAS) offers unprecedented capabilities for distributed monitoring, coordinated decision support, and service integration. However, multi‐agent collaboration simultaneously introduces severe novel vulnerabilities, including topological contamination, privacy leakage, cascading systemic failures, and the socio‐psychological risks of undue algorithmic persuasion. This work introduces a comprehensive, privacy‐preserving, explainable, and human‐centred MAS framework explicitly designed for collaborative clinical decision support. Rejecting the paradigm of a single, omniscient AI model with unrestricted data access, the proposed architecture operationalises clinical AI as a heavily regulated team of specialised, interacting agents. The framework defines six distinct agent classes: Data Stewardship, Modality, Explanation, Consensus, Safety Monitor, and Clinician Interface agents. Furthermore, the implementation is governed by six foundational design principles grounded in classic Autonomous Agents and MAS theory, adapted for modern generative systems. By enforcing minimum‐necessary data exchange, verifiable team‐level explainability, explicit hierarchical escalation, secure communication topologies, and human‐centred safety protocols, this framework provides both a domain‐grounded blueprint and a transferable set of robust design principles for deploying trustworthy MAS in high‐stakes environments.
Funding Information
  • King Fahd University of Petroleum and Minerals (IN26020)
  • Nottingham Trent University (QR‐IPF)

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