Medical AI Multi-Agent Workflow Orchestrator: Enterprise-Grade Clinical Referral System Based on A2A and MCP Protocols

This project is a production-grade proof of concept that demonstrates how to build a multimodal, non-hardcoded multi-agent workflow system based on A2A (Agent-to-Agent Communication Protocol) and MCP (Model Context Protocol) for enterprise-level clinical referral scenarios. It aims to solve the problem that traditional hardcoded referral systems struggle to adapt to complex clinical scenarios, enabling flexible and scalable automated processing.

In the digital transformation of the medical industry, automation of clinical referral processes is crucial. Traditional referral systems use hardcoded business logic and are difficult to adapt to complex and changing clinical scenarios. This project proposes an open protocol-based multi-agent workflow system to achieve flexible and scalable clinical referral automation.

• A2A Protocol: A Google-developed agent-to-agent communication protocol that standardizes interactions between different AI agents (discovery, information exchange, task negotiation, coordinated action), supporting seamless collaboration among specialized agents (image analysis, medical record understanding, etc.) in medical scenarios.
• MCP Protocol: An Anthropic-developed model context protocol that standardizes interactions between large language models and external tools/data sources, enabling secure access to resources like medical records and knowledge bases while maintaining access control and audit trails.

Architecture Design:

1. Protocol-first: All agent interactions use A2A/MCP protocols instead of hardcoded APIs, ensuring high scalability.
2. Multimodal processing: Supports multiple data types such as structured medical records, unstructured text, and medical images, parsed and fused by specialized agents.
3. Dynamic orchestration: Dynamically determines the order of agent calls and collaboration methods based on referral scenarios.

Core Components:

• Referral Reception Agent: Entry point that receives requests and routes them.
• Medical Record Parsing Agent: Extracts structured information from unstructured medical records.
• Image Analysis Agent: Connects to AI services to analyze images and generate reports.
• Specialty Evaluation Agent: Evaluates the suitability of referrals in various specialty fields.
• Coordination and Decision Agent: Synthesizes outputs from all parties to make final decisions.

Technical Innovations:

1. Non-hardcoded logic: Business logic is externalized into configurable protocol interaction modes, allowing adaptation to needs without modifying core code.
2. Explainable decisions: Records the complete reasoning chain to meet regulatory compliance requirements.
3. Human-AI collaboration: Escalates to manual review when agents lack confidence, and feedback is used for optimization.

Application Scenarios:

• Large hospital referral centers: Automatic triage and preprocessing to improve efficiency.
• Regional medical collaboration networks: Seamless flow of referral information across institutions.
• Specialty alliance collaboration: Coordinates referrals between hospitals of different levels.

Challenges:

1. Data privacy and security: Need to continuously follow the latest security standards.
2. Clinical validation and regulation: Need to collect real-world data for compliance certification.
3. Multi-agent collaboration optimization: Need to optimize scheduling algorithms as the number of agents increases.

Outlook: Plan to introduce more intelligent scheduling algorithms to optimize agent call order and parallel strategies.

This project demonstrates the application of cutting-edge AI agent technology in medical scenarios. Through open protocols, multi-agent collaboration, and dynamic orchestration, it provides an innovative solution for the intelligent transformation of healthcare. It has reference value for the fields of medical AI, agent systems, and enterprise-level workflow automation.