Towards Efficient Patient Recruitment for Clinical Trials: Application of a Prompt-Based Learning Model

Our results demonstrate that integrating ontology-based extractive summarization with prompt-based LLMs can substantially improve eligibility classification. The summarization step enhanced model focus and interpretability, particularly for long or ambiguous narratives. This pipeline offers a scalable and adaptable framework for clinical trial automation and has the potential for real-world integration with electronic medical record matching systems.