Retrieval-Augmented LLMs improve clinical trial recruitment by localizing evidence in EHRs

Researchers explored retrieval-augmented large language models (LLMs) for identifying suitable patients for clinical trials from electronic health records. The study evaluated various LLMs, including general and medical-adapted versions, and tested strategies to handle long documents, such as default context windows, NER-based summarization, and dynamic evidence retrieval (RAG). The MedGemma model combined with RAG achieved the highest performance, demonstrating LLMs' potential to improve trial recruitment efficiency, particularly for criteria requiring long-term reasoning. AI

影响 Demonstrates LLMs' potential to streamline clinical trial recruitment by improving evidence localization in patient records.

排序理由 Academic paper detailing novel application of LLMs and retrieval augmentation for a specific problem domain.

在 arXiv cs.CL 阅读 →

• EHR
• LLMs
• MedGemma
• N2C2
• Ziyi Chen

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• 产品

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