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//PROJECT DOSSIER

Context-aware Data-Driven Medical Assistant

Next.js
TypeScript
Django
LLM
Neo4j
MCP
Detailed screenshot of Context-aware Data-Driven Medical Assistant project showing the interface and key features
OVERVIEW

Project Overview

A clinician-facing assistant that answers natural-language questions over Electronic Health Record context, reducing the need to manually search through patient data during clinical work. Patient relationships such as diagnoses, medications, vitals and lab results are modelled in Neo4j, connected to a Python backend, Next.js interface and LLM orchestration layer.

ARCHITECTURE

System Architecture

Architecture diagram for Context-aware Data-Driven Medical Assistant showing system components and their interactions
KEY FEATURES

Key Features

  • 01Multi-chat UI with speech-to-text capabilities and markdown-formatted replies
  • 02Seven domain modes (vitals, labs, etc.) with automatic prompt slice loading
  • 03Structured-chat agent guaranteeing JSON tool calls with retry mechanisms for parse errors
  • 04Live Neo4j query execution with safety rules and lower-case literal enforcement
  • 05FastMCP transport layer for efficient communication between components

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