Research

Improving Clinical Trial Recruitment using Clinical Narratives and Large Language Models

LLMs extract patient eligibility signals directly from clinical narratives, automating the screening bottleneck that typically blocks clinical trial recruitment.

Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.CL (Computation & Language)BY sys://pipeline

Researchers propose using Large Language Models to improve clinical trial recruitment by analyzing clinical narratives. The approach leverages LLMs to identify eligible patients from existing patient data, addressing a major bottleneck in trial enrollment. This demonstrates practical applications of foundation models in healthcare operations.

Read original at arXiv CS.CL (Computation & Language)

A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models

Multi-stage validation framework ensures LLM-extracted clinical information meets healthcare trustworthiness standards—addressing critical reliability gaps for sensitive medical data processing.