Performance of Retrieval-Augmented Large Language Models to Recommend Head and Neck Cancer Clinical Trials
Year: 2024
Type: article
Abstract: UNSTRUCTURED In this study, we evaluated the performance of a retrieval-augmented language model, powered by GPT-4, to recommend appropriate clinical trial recommendations for a head & neck cancer population at the Memorial Sloan Kettering Cancer Center. We demonstrated that retrieval-augmented LLM could achieve moderate performance, exceeding the historical performance of untrained LLMs to provide oncology treatment recommendations by 4-20 folds. Our study provided insights into the rarely measured performance of retrieval-augmented LLM using real-world patient cases in comparison to physician expert recommendations.
DOI: 10.2196/60695
Institutions: Memorial Sloan Kettering Cancer Center, Columbia University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,950
OpenAlex ID: W4402245094