MCP-AI: Protocol-Driven Intelligence Framework for Autonomous Reasoning in Healthcare
Zag ElSayed, Craig Erickson, Ernest Pedapati
TL;DR
MCP-AI introduces a novel, protocol-driven architecture that unites the Model Context Protocol with healthcare reasoning to address the shortcomings of static CDSS and stateless LLMs. By capturing patient state, objectives, reasoning history, and validation in versioned MCP files, it enables persistent memory, multi-agent collaboration, and physician-in-the-loop oversight across longitudinal care. The approach is demonstrated through Fragile X syndrome with comorbid depression and chronic diabetes/hypertension care, showing auditable decision-making, regulatory alignment, and seamless HL7/FHIR integration. This framework offers a scalable, interpretable, and safety-focused pathway toward autonomous clinical reasoning that can adapt across care settings and specialties.
Abstract
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.