Mission-critical healthcare applications including real-time intensive care monitoring, ambulance-to-hospital orchestration, and distributed medical imaging inference require workflow-level, time-bounded coordination across heterogeneous devices, edge servers, and network control entities. While current 3GPP and O-RAN standards excel at per-device control and quality-of-service enforcement, they do not natively expose abstractions for workflow-level coordination under strict clinical timing constraints, leaving this capability to fragile, application-specific overlays. This article outlines the Collective Adaptive Intelligence Plane (CAIP) as a standards-aligned coordination framework that addresses this abstraction gap without introducing new protocol layers. CAIP is realized through minimal, backward-compatible coordination profiles anchored to existing RRC, QoS/SDAP, and O-RAN E2 interfaces, enabling workflow-scoped coordination context binding, deadline-aware coordination pacing, semantic flow association, and privacy-preserving data locality across distributed clinical entities. We analyze the structural limitations of existing standards, present a concrete interface mapping to 3GPP and O-RAN mechanisms, illustrate deployment through a representative ICU coordination scenario, and outline a phased standardization roadmap from proof-of-concept xApp deployment to AI-native 6G specification evolution. The proposed framework is incrementally deployable on current 5G Advanced infrastructure and provides a principled migration path toward workflow-level coordination abstraction as a first-class capability in future 6G healthcare networks.

翻译：实时重症监护监测、救护车至医院协同调度以及分布式医学影像推理等关键医疗应用，需要在异构设备、边缘服务器及网络控制实体之间实现工作流级别、时间约束的协调。尽管当前3GPP与O-RAN标准在单设备控制和服务质量保障方面表现优异，但其并未原生提供满足严格临床时序约束的工作流级协调抽象能力，导致该功能只能依赖脆弱且应用特定的覆盖层实现。本文提出集体自适应智能平面（CAIP）作为一种标准对齐的协调框架，旨在不引入新协议层的前提下弥补这一抽象鸿沟。CAIP通过基于现有RRC、QoS/SDAP和O-RAN E2接口的最小化向后兼容协调配置文件实现，支持工作流范围的协调上下文绑定、截止时间感知的协调步调控制、语义流关联以及跨分布式临床实体的隐私保护数据本地化处理。我们分析了现有标准的结构性局限，提出了与3GPP及O-RAN机制的具体接口映射方案，通过典型ICU协调场景阐释部署流程，并规划了从概念验证xApp部署到AI原生6G标准演进的阶段性标准化路线图。该框架可在现有5G Advanced基础设施上逐步部署，并为未来6G医疗网络中将工作流级协调抽象作为原生能力提供了系统化的演进路径。