Evaluating large language models for information extraction from gastroscopy and colonoscopy reports through multi-strategy prompting.

Abstract

To systematically evaluate large language models (LLMs) for automated information extraction from gastroscopy and colonoscopy reports through prompt engineering, addressing their ability to extract structured information, recognize complex patterns, and support diagnostic reasoning in clinical contexts. We developed an evaluation framework incorporating three hierarchical tasks: basic entity extraction, pattern recognition, and diagnostic assessment. The study utilized a dataset of 162 endoscopic reports with structured annotations from clinical experts. Various language models, including proprietary, emerging, and open-source alternatives, were evaluated under both zero-shot and few-shot learning paradigms. For each task, multiple prompting strategies were implemented, including direct prompting and five Chain-of-Thought (CoT) prompting variants. Larger models with specialized architectures achieved better performance in entity extraction tasks but faced notable challenges in capturing spatial relationships and integrating clinical findings. The effectiveness of few-shot learning varied across models and tasks, with larger models showing more consistent improvement patterns. These findings provide important insights into the current capabilities and limitations of language models in specialized medical domains, contributing to the development of more effective clinical documentation analysis systems.