MCP-AI：面向医疗领域自主推理的协议驱动智能框架
MCP-AI: Protocol-Driven Intelligence Framework for Autonomous Reasoning in Healthcare
AI总结：
MCP-AI是一种基于模型上下文协议的医疗自主推理框架，通过整合临床逻辑和安全协作，提升医疗决策的可解释性和适应性。
摘要：
医疗AI系统长期以来面临将上下文推理、长期状态管理以及可由人类验证的工作流整合到一个统一框架中的挑战。本文介绍了一种完全创新的架构和概念：将模型上下文协议（MCP）与特定的临床应用相结合，称为MCP-AI。这种整合使智能代理能够在较长时间内进行推理，安全地协作，并遵循真实的临床逻辑，代表着传统临床决策支持系统（CDSS）和基于提示的大型语言模型（LLMs）的重大转变。随着医疗系统日益复杂，对自主、上下文感知的临床推理框架的需求变得紧迫。我们提出了MCP-AI，这是一种基于模型上下文协议（MCP）的新型可解释医疗决策制定架构，MCP是一种模块化、可执行的规范，用于在实时工作流中协调生成性和描述性AI代理。每个MCP文件捕获临床目标、患者上下文、推理状态和任务逻辑，形成可重用和可审计的记忆对象。与传统CDSS或无状态提示式AI系统不同，MCP-AI支持适应性、长期性和跨护理场景的协作推理。MCP-AI通过两个用例进行了验证：（1）具有共病抑郁的弗里吉亚综合征诊断建模，以及（2）2型糖尿病和高血压的远程协调。在任何情况下，该协议都促进医生在场验证，简化临床流程，并确保在医疗提供者之间安全转移AI责任。系统连接到HL7/FHIR接口，并遵守HIPAA和FDA SaMD指南等监管标准。MCP-AI为未来临床环境中的可解释、可组合和安全导向的AI提供了可扩展的基础。
英文摘要：
Healthcare AI systems have historically faced challenges in merging contextual reasoning, long-term state management, and human-verifiable workflows into a cohesive framework. This paper introduces a completely innovative architecture and concept: combining the Model Context Protocol (MCP) with a specific clinical application, known as MCP-AI. This integration allows intelligent agents to reason over extended periods, collaborate securely, and adhere to authentic clinical logic, representing a significant shift away from traditional Clinical Decision Support Systems (CDSS) and prompt-based Large Language Models (LLMs). As healthcare systems become more complex, the need for autonomous, context-aware clinical reasoning frameworks has become urgent. We present MCP-AI, a novel architecture for explainable medical decision-making built upon the Model Context Protocol (MCP) a modular, executable specification for orchestrating generative and descriptive AI agents in real-time workflows. Each MCP file captures clinical objectives, patient context, reasoning state, and task logic, forming a reusable and auditable memory object. Unlike conventional CDSS or stateless prompt-based AI systems, MCP-AI supports adaptive, longitudinal, and collaborative reasoning across care settings. MCP-AI is validated through two use cases: (1) diagnostic modeling of Fragile X Syndrome with comorbid depression, and (2) remote coordination for Type 2 Diabetes and hypertension. In either scenario, the protocol facilitates physician-in-the-loop validation, streamlines clinical processes, and guarantees secure transitions of AI responsibilities between healthcare providers. The system connects with HL7/FHIR interfaces and adheres to regulatory standards, such as HIPAA and FDA SaMD guidelines. MCP-AI provides a scalable basis for interpretable, composable, and safety-oriented AI within upcoming clinical environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
