Harvard Unveils New Knowledge Graph Agent for improved AI in Medicine. Called KGARevion, it combines the knowledge from knowledge graphs with the knowledge of LLMs.

Since RAG suffers from inaccurate and incomplete retrieval problems in medicine, Harvard et al present a new and improved methodology to significantly increase the reasoning performance of medical AI systems. Special focus on complex medical human interactions.

New insights and new methods to combine the non-codified knowledge of LLMs with the structural codified knowledge of medical knowledge graphs.

Detailed explanation of the new methods in this AI research pre-print (also for beginners in AI).

All rights w/ authors:
KNOWLEDGE GRAPH BASED AGENT FOR COMPLEX,
KNOWLEDGE-INTENSIVE QA IN MEDICINE
https://arxiv.org/pdf/2410.04660

00:00 Harvard has a problem w/ LLMs and RAG
04:20 Harvard Univ develops a new solution
07:24 The Generate Phase (medical triplets)
09:50 Review Phase of KGARevion
12:30 Multiple embeddings from LLM and Graphs
15:40 Alignment of all embeddings in common math space
20:48 Dynamic update of the Knowledge graph
21:52 Update LLM with grounded graph knowledge
23:15 Revise phase to correct incomplete triplets
25:20 Answer phase brings it all together
26:07 Summary
29:52 Performance analysis
33:39 All prompts for KGARevion in detail

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