AI Research Assistant Platform
Context-Aware Paper Analysis & Visualization for researchers and medical professionals.
Project Overview
Developed an intelligent AI-driven platform to assist researchers and medical professionals by extracting, summarizing, and visualizing critical insights from clinical and scientific papers. This system streamlines literature review by using semantic search, trial phase extraction, and visual entity mapping.
The platform uses LLM-based Retrieval-Augmented Generation (RAG) to process research content and provides summarized introductions, methodology analysis, trial phase classification, and entity extractions with confidence scoring and editable results.
Key Features
- AI Summarization Engine with GPT-like models
- Trial Phase Detection and classification
- Semantic Search & Copy-Paste Parsing
- Vector Search & Retrieval (FAISS)
- Graph-Based Visualization with Neo4j
- Confidence Scores & Editable Fields
AI & Analysis Features
AI Summarization
- • GPT-like models for content analysis
- • Reranking for improved accuracy
- • Concise summary extraction
- • Methodology analysis
- • Key findings identification
Trial Phase Detection
- • Phase 1/2/3 classification
- • Clinical trial outcomes
- • Study metadata extraction
- • Trial phase highlighting
- • Outcome analysis
Entity Recognition
- • Drug name extraction
- • Biomarker identification
- • Endpoint detection
- • Medical terminology
- • Entity relationship mapping
Search & Visualization
Semantic Search
- • Context-aware parsing
- • Raw text processing
- • Full paper upload support
- • Entity recognition
- • Real-time similarity search
Knowledge Graph
- • Dynamic relationship charts
- • Drug-trial-entity linkage
- • Interactive visualizations
- • Cross-paper connections
- • Graph-based insights
Technical Implementation
Backend & AI
- • Python (Django REST Framework)
- • LangChain for LLM integration
- • Hugging Face Transformers
- • OpenAI API integration
- • Azure RAG model
Data & Storage
- • FAISS for vector storage
- • Neo4j for knowledge graphs
- • Azure Blob for documents
- • Azure Document Intelligence
- • Structured parsing
Frontend & Cloud
- • React.js with D3.js/Chart.js
- • Interactive dashboards
- • Azure App Services
- • JWT-based authentication
- • Researcher/admin privileges
Core Skills Demonstrated
Python & Django
Backend development with RESTful APIs
LangChain
LLM integration and RAG architecture
FAISS
Vector database and similarity search
Neo4j
Knowledge graph and relationship mapping
React
Interactive frontend with data visualization
Azure Document Intelligence
AI-powered document processing
Use Case & Benefits
For Researchers
- • Streamlined literature review process
- • Quick extraction of key insights
- • Trial phase classification
- • Entity relationship discovery
- • Custom report saving and export
Technical Innovation
- • Advanced AI/ML integration
- • Real-time processing capabilities
- • Scalable architecture
- • Confidence scoring system
- • Interactive visualizations
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