Using Retrieval-Augmented Generation to Capture Molecularly-Driven Treatment Relationships for Precision Oncology

Modern generative artificial intelligence techniques like retrieval-augmented generation (RAG) may be applied in support of precision oncology treatment discussions. Experts routinely review published literature for evidence and recommendations of treatments in a labor-intensive process. A RAG pipel...

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Bibliographic Details
Published inStudies in health technology and informatics Vol. 316; p. 983
Main Authors Kreimeyer, Kory, Canzoniero, Jenna V, Fatteh, Maria, Anagnostou, Valsamo, Botsis, Taxiarchis
Format Journal Article
LanguageEnglish
Published Netherlands 22.08.2024
Subjects
Artificial Intelligence
Data Mining - methods
Humans
Information Storage and Retrieval - methods
Medical Oncology
Natural Language Processing
Neoplasms - therapy
Precision Medicine
Precision Oncology
Retrieval-Augmented Generation
Large Language Models
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Summary:Modern generative artificial intelligence techniques like retrieval-augmented generation (RAG) may be applied in support of precision oncology treatment discussions. Experts routinely review published literature for evidence and recommendations of treatments in a labor-intensive process. A RAG pipeline may help reduce this effort by providing chunks of text from these publications to an off-the-shelf large language model (LLM), allowing it to answer related questions without any fine-tuning. This potential application is demonstrated by retrieving treatment relationships from a trusted data source (OncoKB) and reproducing over 80% of them by asking simple questions to an untrained Llama 2 model with access to relevant abstracts.
ISSN:1879-8365