CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care

TL;DR

CancerKG.ORG is a large, interactive hybrid knowledge graph and LLM system designed to improve cancer treatment and research by providing up-to-date, verifiable medical knowledge with multiple user interfaces.

Contribution

This work introduces one of the first web-scale hybrid KG-LLM systems for cancer, combining automatic knowledge ingestion with LLMs and multiple tailored interfaces.

Findings

Outperforms standalone LLMs, KGs, or search engines in user needs.

Automatically ingests and updates latest medical findings.

Serves as a reliable retrieval-augmented generation system.

Abstract

Here, we describe one of the first Web-scale hybrid Knowledge Graph (KG)-Large Language Model (LLM), populated with the latest peer-reviewed medical knowledge on colorectal Cancer. It is currently being evaluated to assist with both medical research and clinical information retrieval tasks at Moffitt Cancer Center, which is one of the top Cancer centers in the U.S. and in the world. Our hybrid is remarkable as it serves the user needs better than just an LLM, KG or a search-engine in isolation. LLMs as is are known to exhibit hallucinations and catastrophic forgetting as well as are trained on outdated corpora. The state of the art KGs, such as PrimeKG, cBioPortal, ChEMBL, NCBI, and other require manual curation, hence are quickly getting stale. CancerKG is unsupervised and is capable of automatically ingesting and organizing the latest medical findings. To alleviate the LLMs shortcomings, the verified KG serves as a Retrieval Augmented Generation (RAG) guardrail. CancerKG exhibits 5 different advanced user interfaces, each tailored to serve different data modalities better and more convenient for the user.