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AI-clinical-trial-protocol-screener
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Inspiration
Clinical trial documents are often lengthy and complex, making it difficult for researchers and healthcare professionals to quickly identify eligibility criteria and important protocol information. We wanted to simplify this process using AI and automation.
What it does
Clinical Trial Protocol Screener is an AI-powered application that analyzes clinical trial protocols using Large Language Models (LLMs), NLP, and machine learning techniques. It extracts important information such as inclusion/exclusion criteria and helps streamline clinical research workflows.
How we built it
We built the application using Python, NLP libraries, and trained AI/LLM models for intelligent document understanding. The frontend and backend were integrated into a web-based interface and deployed using Docker on Hugging Face Spaces for scalable access.
Challenges we ran into
- Processing complex medical terminology accurately
- Handling unstructured clinical documents
- Optimizing AI model responses for faster screening
- Deploying and containerizing the application using Docker
Accomplishments that we're proud of
- Successfully developed an AI-powered healthcare screening system
- Integrated LLM-based protocol analysis
- Built a scalable Dockerized deployment
- Created an easy-to-use interface for clinical protocol evaluation
What we learned
- Practical implementation of LLMs and NLP in healthcare
- Docker-based deployment workflows
- Handling real-world medical document processing
- Improving AI response accuracy and efficiency
What's next for clinical-trial-protocol
- Advanced medical entity recognition
- Multi-document protocol comparison
- Real-time healthcare database integration
- Enhanced AI recommendations and analytics
- Improved scalability and performance
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