ETHOS: Towards a Modular Ethics Framework for Clinical Multi-Agent Systems

2026-08-15

AI Summary

This study addresses the absence of ethical governance mechanisms in clinical multi-agent systems by proposing ETHOS, a modular ethical framework. Designed as a plug-and-play meta-agent, ETHOS integrates seamlessly into existing architectures and introduces a novel three-tier runtime supervision mechanism that combines deterministic rules, contextual review, and ethical critique. This approach translates abstract principles into executable checks within a dynamic feedback loop. Validation in a liver disease decision-support system demonstrates that ETHOS effectively identifies evidentiary deficiencies, intercepts unsafe responses, and appropriately refuses to answer when necessary. Consequently, this work successfully operationalizes high-level ethical principles into deployable and auditable safety safeguards for clinical AI, significantly enhancing decision-making reliability.

Abstract

The rapid adoption of large language models has enabled the development of clinical multi-agent systems (MAS) capable of integrating multimodal patient data and supporting increasingly complex clinical decision-making. However, the deployment of these systems in real-world healthcare settings raises critical ethical concerns related to safety, fairness, accountability, transparency, and patient trust. While numerous organizations, including the World Health Organization, the National Academy of Medicine, and the FUTURE-AI consortium, have proposed ethical frameworks and governance principles for healthcare AI, these efforts remain largely conceptual. To address this challenge, we present ETHOS (Ethics and Trust through Hierarchical Oversight System), a modular ethics framework designed as a governance meta-agent that can be integrated with any existing multi-agent system without requiring changes to its underlying architecture. ETHOS translates stakeholder-informed ethical requirements into executable runtime oversight through a layered governance approach consisting of deterministic checks, contextual reviews, and a final ethics critic. These components continuously evaluate intermediate reasoning steps and final outputs, enabling the system to identify ethical risks, request revisions, or suppress responses that fail predefined safety and trustworthiness criteria. We demonstrate ETHOS within a hepatology clinical decision-support MAS. Results show that ETHOS improves decision reliability by detecting incomplete, inconsistent, or out-of-scope evidence and appropriately increasing abstention when safe recommendations cannot be supported. By embedding ethical governance directly into system operation, ETHOS provides a practical and auditable mechanism for transforming high-level AI ethics principles into deployable safeguards.

Problem

• Clinical Multi-Agent Systems
• AI Ethics
• Ethical Governance
• Healthcare AI Safety
• Trustworthiness

Innovation

• Modular Ethics Framework
• Governance Meta-Agent
• Hierarchical Oversight System
• Runtime Ethical Governance
• Clinical Multi-Agent Systems