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
IMPACT Demonstrates LLMs' potential to streamline clinical trial recruitment by improving evidence localization in patient records.
RANK_REASON Academic paper detailing novel application of LLMs and retrieval augmentation for a specific problem domain.
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