MCP-AI: Protocol-Driven Intelligence Framework for Autonomous Reasoning in Healthcare

2025-12-04

AI Summary

Medical AI faces critical challenges including weak contextual reasoning, inability to maintain long-term clinical state, and lack of verifiable decision-making. To address these, this paper proposes MCP-AI—a novel framework built upon the Model Context Protocol (MCP) to enable autonomous, interpretable, and cross-scenario clinical reasoning, overcoming limitations of both traditional clinical decision support systems (CDSS) and stateless large language models. MCP-AI modularizes clinical logic into executable protocol files, enabling auditability, human-AI collaborative validation, and seamless integration with HL7/FHIR standards—ensuring compliance with HIPAA and FDA Software as a Medical Device (SaMD) regulations. Empirical evaluation in Fragile X syndrome diagnosis and remote diabetes-hypertension management demonstrates significant improvements in workflow efficiency, safe responsibility handover between clinicians and AI, and end-to-end traceable, accountable AI decisions. MCP-AI establishes a new architectural paradigm for trustworthy, regulatory-compliant medical AI.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications

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.

Problem

• Develops a framework for autonomous clinical reasoning with long-term state management
• Integrates modular protocols to enable secure, collaborative, and explainable medical decision-making
• Validates an adaptive AI system for complex, multi-condition healthcare scenarios

Innovation

• Combines Model Context Protocol with clinical applications
• Uses modular executable specifications for AI orchestration
• Supports adaptive longitudinal reasoning across care settings