Generative AI Medical Document Summarizer & Clinical Diagnostic Assistant (RAG)
Healthcare Retrieval-Augmented Generation (RAG) assistant using LangChain, Vector DB, and LLMs for doctor report synthesis.
Problem Statement
Physicians and healthcare specialists spend over 35% of their working hours scanning lengthy multi-page patient EHRs, clinical lab findings, and discharge summaries, causing diagnostic fatigue.
Proposed Solution
A HIPAA-conscious Retrieval-Augmented Generation (RAG) platform that indexes PDF clinical records, computes dense semantic embeddings in ChromaDB, and delivers instant, hallucination-guarded clinical summaries with exact page-source citations.
Key System Features
System Architecture & Pipeline
End-to-end data transformation pipeline from input capture to output visualization.
Project Modules Breakdown
Document Ingestion & Chunking Pipeline
Module 1Extracts clinical text, parses tabular lab values, and chunks content using recursive character splitting.
Vector Store & Semantic Retrieval
Module 2Generates high-dimensional vector embeddings stored in a local persistent ChromaDB instance.
Clinical LLM Synthesis Engine
Module 3Orchestrates prompt templates with temperature 0.1 for high factual fidelity clinical answers.
Modern Next.js Diagnostic UI
Module 4Split-screen interface displaying original PDF document alongside AI diagnostic insights.
Complete Technology Stack
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Package Deliverables
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