LLM-Powered Clinical Trial Matcher

Executive summary

The recruitment of patients for eligible clinical trials accelerates the development of life-saving therapeutics. However, life science organizations and health systems often struggle to match patients to trials successfully, making recruitment itself a costly and resource-intensive process. When recruitment efforts are unsuccessful, development is delayed, expenses increase, and studies may ultimately fail.

The LLM-Powered Clinical Trial Matcher lab demonstrates a practical application of retrieval-augmented generation (RAG) using large language models (LLMs) to improve patient recruitment for clinical trials. This system showcases how RAG can be repurposed to interpret unstructured eligibility text and dynamically filter patient datasets based on those criteria. By automating eligibility translation and matching logic, the approach highlights how LLMs can reduce manual effort, improve consistency and support scalable clinical trial recruitment systems.