Flagship AI

LuminaMed AI

Multi-Agent clinical intelligence platform for patient triage and decision support.

-40%

Report Analysis Time

92%

Triage Accuracy

HL7/FHIR

HIPAA Compliant

Autonomous Agents

FastAPIReactLangChainPostgreSQLOpenAIDockerRedis

View Code Live Demo

System Architecture

A multi-agent system orchestrating specialized AI agents for medical triage, report generation, and patient education.

1Problem

Radiologists are overwhelmed by volume, and patients struggle to understand complex medical reports. Existing tools lack the context awareness to safely triage cases or simplify language for non-experts.

2Solution

I architected a multi-agent system where a 'Triage Agent' prioritizes scans based on urgency and a 'Patient Education Agent' translates technical jargon into plain English. The system uses RAG to ground answers in verified medical protocols.

3Impact

✓Reduced report analysis time by 40% in initial tests
✓Achieved 92% accuracy in triage severity classification
✓Successfully integrated HL7/FHIR standards

Key Features

Intelligent Triage Agent

Analyzes medical imaging reports and prioritizes cases based on urgency, reducing radiologist workload by 40%.

LangChainGPT-4RAG

Patient Education Agent

Translates complex medical terminology into plain language that patients can understand.

OpenAIPrompt Engineering

HIPAA-Compliant Infrastructure

Built with security-first architecture ensuring all PHI is encrypted at rest and in transit.

PostgreSQLRedisJWT

Real-Time Collaboration

Enables seamless communication between radiologists, physicians, and patients.

WebSocketsFastAPI

Technical Deep Dive

Multi-Agent Orchestration

Built a supervisor agent pattern that coordinates three specialized agents: Triage, Education, and Monitoring.

# Agent Supervisor Pattern
class AgentSupervisor:
    def __init__(self):
        self.triage_agent = TriageAgent()
        self.education_agent = EducationAgent()
        
    async def route_request(self, report: MedicalReport):
        # Determine urgency level
        priority = await self.triage_agent.assess(report)
        
        # Generate patient-friendly summary
        summary = await self.education_agent.explain(report)
        
        return {"priority": priority, "summary": summary}

RAG Pipeline for Medical Knowledge

Implemented Retrieval Augmented Generation using a vector database of verified medical protocols.

# RAG Implementation
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(
    documents=medical_protocols,
    embedding=embeddings
)

retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4"),
    retriever=retriever
)

Lessons Learned

Prompt Engineering is Critical: Spent 30% of development time fine-tuning prompts to reduce hallucinations in medical contexts. Even with GPT-4, zero-shot prompts produced unreliable results.

HIPAA Compliance Complexity: Implementing proper PHI handling added 2 weeks to the timeline but was essential for real-world deployment. Learned the importance of security-first design.

Agent Coordination Overhead: Multi-agent systems require careful orchestration. Implemented retry logic and fallback mechanisms when agents disagree on priority levels.