Large language models improve the identification of emergency department visits for symptomatic kidney stones.

ABSTRACT: Recent advancements of large language models (LLMs) like generative pre-trained transformer 4 (GPT-4) have generated significant interest among the scientific community. Yet, the potential of these models to be utilized in clinical settings remains largely unexplored. In this study, we investigated the abilities of multiple LLMs and traditional machine learning models to analyze emergency department (ED) reports and determine if the corresponding visits were due to symptomatic kidney stones. Leveraging a dataset of manually annotated ED reports, we developed strategies to enhance LLMs including prompt optimization, zero- and few-shot prompting, fine-tuning, and prompt augmentation. Further, we implemented fairness assessment and bias mitigation methods to investigate the potential disparit

SUBMITTER: Bejan CA 

PROVIDER: S-EPMC11775227 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature