Smarter Medical Diagnosis with Adaptive RAG
Fine-tuning LLM retrieval based on information density
FIND introduces a novel approach to medical diagnosis using Large Language Models that dynamically adjusts retrieval based on task complexity and information needs.
- Evaluates task difficulty to determine when retrieval is necessary
- Uses fine-grained information density to guide retrieval decisions
- Balances efficiency and accuracy for clinical diagnosis requirements
- Outperforms existing RAG methods in medical diagnosis tasks
This research matters because it addresses the critical challenge of efficient clinical decision support, potentially reducing unnecessary retrievals while maintaining diagnostic accuracy in real-world healthcare settings.