Improving Clinical Trial Recruitment using Clinical Narratives and Large Language Models

Researchers have developed a method using large language models (LLMs) to enhance patient screening in clinical trials, addressing the bottleneck of under-enrollment. The study found that MedGemma model with RAG strategy achieved the highest micro-F1 score of 89.05%, demonstrating significant improvements in handling long-term reasoning across extensive medical documents. This advancement could streamline trial recruitment processes and improve overall efficiency for developers working on AI solutions in healthcare.

Read the full article at arXiv cs.CL (NLP)

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