RadOncRAG: A Novel Retrieval-Augmented Generation Framework Improves Large Language Model Benchmark Performance in Radiation Oncology.

Nikhil Gautam Thaker^1,2, Navid Redjal¹, Adam Dicker³, Arturo Loaiza-Bonilla^4,5, Trevor Royce⁶, Vivek Subbiah⁷, Vikash Deendyal¹, Jonathan R Gabriel⁸, Neena Shetty¹, Ajay Choudhri¹, Gautam H Thaker²

¹Capital Health, Pennington, NJ.

²Bayta Systems, Newtown, PA.

³Thomas Jefferson, Philadelphia, PA.

⁴St Luke's University Health Network, Easton, PA.

⁵Massive Bio, New York, NY.

⁶Atrium Health Wake Forest Baptist, Winston Salem, NC.

⁷Sarah Cannon Research Institute, Nashville, TN.

⁸University of Arizona, Tucson, AZ.

PubMed DOI

Abstract

PURPOSE: Large language models (LLMs) show promise in assisting knowledge-intensive fields such as oncology, where up-to-date information and multidisciplinary expertise are critical. Traditional LLMs risk hallucinations and reliance on static, possibly outdated data that lack domain-specific context. Retrieval-augmented generation (RAG) has emerged as a strategy to address these issues by incorpo…

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