📚 Main Topics

1. Current Challenges with LLMs in Medicine

   • Issues with incorrect retrieval and misalignment with medical knowledge.
   • Difficulty in combining structured (codified) and non-structured (non-codified) knowledge.
   • Performance metrics of existing models (e.g., Llama 3B, Llama 38B, GPT-4 Turbo).

2. Proposed Solutions

   • Development of a knowledge graph-based agent for complex medical queries.
   • Integration of LLMs with knowledge graphs to enhance reasoning capabilities.

3. Methodology Overview

   • Four-phase process: Generate, Review, Revise, and Answer.
   • Use of Unified Medical Language System (UMLS) codes for accurate mapping of medical terms.
   • Alignment of LLM embeddings with knowledge graph embeddings.

4. Results and Performance

   • Improvement in accuracy of medical reasoning tasks.
   • Comparison of performance metrics before and after applying the new methodology.

5. Future Directions

   • Potential for further enhancements and applications in medical AI.
   • Discussion of options for updating knowledge graphs and improving LLMs.

✨ Key Takeaways

• LLMs Struggle with Medical ReasoningCurrent LLMs show inadequate performance in complex medical reasoning tasks, often falling below acceptable accuracy levels.
• Knowledge Graph IntegrationThe proposed methodology emphasizes the integration of LLMs with structured knowledge from knowledge graphs to improve reasoning and accuracy.
• Four-Phase MethodologyThe process involves generating triplets from medical queries, reviewing their correctness against a knowledge graph, revising incorrect triplets, and finally selecting the best answer based on validated information.
• UMLS Codes as a BridgeUMLS codes serve as a crucial intermediary to ensure consistent mapping of medical concepts between LLMs and knowledge graphs.
• Performance ImprovementThe new methodology has shown significant improvements in reasoning accuracy, particularly as the complexity of medical concepts increases.

🧠 Lessons Learned

• Importance of Structured KnowledgeCombining the free-form reasoning capabilities of LLMs with structured knowledge from graphs can lead to more effective medical AI systems.
• Iterative Error CorrectionThe revision phase allows for continuous improvement of generated triplets, enhancing the robustness of the answers provided by the system.
• Future PotentialThere are numerous opportunities to further develop and refine this methodology, potentially leading to more advanced medical AI applications.