Retrieval-Augmented Generation for Clinical Research
We're revolutionizing clinical research by making advanced AI accessible to researchers, accelerating drug discovery, and ultimately improving patient outcomes worldwide.
What is RAG?
Retrieval-Augmented Generation (RAG) is an AI technique that combines the power of large language models with your specific documents to provide accurate, grounded responses.
The Problem with Traditional AI
Hallucinations
AI can generate plausible but incorrect information
No Source Attribution
Impossible to verify where information came from
Generic Knowledge
Cannot access your specific research documents
How RAG Solves This
Grounded Responses
Answers are based only on your uploaded documents
Full Citations
Every response includes page and section references
Your Knowledge Base
AI works with your specific clinical research data
Your Question
"What were the primary endpoints in cardiovascular trials?"
AI Response: Based on the cardiovascular trials in your documents, the primary endpoints were:
• Major adverse cardiac events (MACE) – Study A, p.23
• Time to cardiovascular death – Study B, p.45
• Time to cardiovascular death – Study B, p.45
How RAG Works
A step-by-step breakdown of our RAG implementation
Document Upload
Upload your clinical trial PDFs to our secure, HIPAA-compliant platform
- Secure file processing with enterprise encryption
- Support for PDF files up to 50MB
- Automatic text extraction and OCR
- Metadata preservation and indexing
Text Processing
AI breaks down documents into searchable chunks while preserving context
- Intelligent text segmentation
- Medical terminology recognition
- Context-aware chunking
- Table and figure extraction
Knowledge Storage
Processed content is stored in our specialized vector database
- Vector embeddings for semantic search
- Hierarchical document structure
- Fast retrieval algorithms
- Source attribution tracking
Document Upload
When you ask questions, the system finds the most relevant information
- Semantic similarity matching
- Multi-document cross-referencing
- Relevance scoring and ranking
- Context-aware filtering
AI Generation
GPT-4 generates accurate answers using only your document content
- Grounded responses with citations
- Statistical analysis integration
- Multi-source synthesis
- Confidence scoring
RAG Use Cases in Clinical Research
Real-world applications of RAG technology for researchers
Literature Review
Quickly analyze hundreds of research papers to identify key findings and trends
Example Query: "Compare efficacy rates across Phase III trials for diabetes medications"
Regulatory Submissions
Extract specific data points required for FDA submissions and reports
Example Query: "Find all adverse events reported in studies between 2020-2024"
Meta-Analysis
Synthesize data from multiple studies for comprehensive analysis
Example Query: "Calculate pooled effect sizes from cardiovascular outcome studies"
Protocol Development
Reference similar studies to inform new clinical trial designs
Example Query: "What inclusion criteria were used in successful oncology trials?"
RAG Use Cases in Clinical Research
Real-world applications of RAG technology for researchers
85% Time Savings
Reduce literature review time from weeks to hours
100% Accuracy
All responses are grounded in your uploaded documents
Source Citations
Every answer includes exact page and section references
Multi-Document
Query across hundreds of documents simultaneously