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Performance of Retrieval-Augmented Language Model to Recommend Head and Neck Cancer Clinical Trials
Tony Kin Wai Hung;
Gilad J. Kuperman;
Eric J Sherman;
Alan L. Ho;
Chunhua Weng;
David G. Pfister;
Jun J. Mao
ABSTRACT
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.
Citation
Please cite as:
Hung TKW, Kuperman GJ, Sherman EJ, Ho AL, Weng C, Pfister DG, Mao JJ
Performance of Retrieval-Augmented Large Language Models to Recommend Head and Neck Cancer Clinical Trials