ETHOS为临床多智能体系统提供可嵌入的伦理监管框架，提升决策安全与可信度。
ETHOS: Towards a Modular Ethics Framework for Clinical Multi-Agent Systems
- 构建分层治理机制，通过确定性检查、上下文审查和伦理评判实现运行时监督。
- 在肝病决策系统中验证，显著提升决策可靠性，识别不完整或越界证据并合理拒绝推荐。
- 无需修改原系统架构，适合医疗AI部署者、伦理审核方及政策制定者参考。
大型语言模型的快速应用推动了能够整合多模态患者数据并支持复杂临床决策的临床多智能体系统（MAS）的发展。然而，这些系统在真实医疗环境中的部署引发了安全、公平、问责、透明度和患者信任等关键伦理问题。尽管世界卫生组织、美国国家医学院及FUTURE-AI联盟等机构提出了多项伦理框架与治理原则，但多数仍停留在概念层面。为此，我们提出ETHOS（伦理与信任的分层监督系统），一种模块化伦理框架，作为可集成于任意现有MAS的治理元智能体，无需修改底层架构。ETHOS将利益相关方定义的伦理要求转化为运行时可执行的监督机制，采用分层治理策略：确定性检查、上下文审查与最终伦理评判。这些组件持续评估中间推理步骤与最终输出，识别伦理风险，请求修正或抑制不符合预设安全与可信标准的响应。我们在肝病临床决策支持MAS中验证ETHOS，结果表明其能有效检测不完整、不一致或越界证据，并在无法安全推荐时合理增加弃权率，显著提升决策可靠性。通过将伦理治理直接嵌入系统运行，ETHOS为将高层级人工智能伦理原则转化为可部署的防护机制提供了实用且可审计的方案。
原文摘要 · Abstract (English)
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.
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