Patient Trial Matching

Patient trial matching aims to efficiently connect patients with suitable clinical trials, a crucial but currently laborious process. Recent research heavily utilizes large language models (LLMs) and other machine learning techniques, such as tree-based memory networks, to automate this process by analyzing unstructured patient data and trial eligibility criteria, often incorporating strategies to improve speed, cost-effectiveness, and interpretability of results. These advancements promise to significantly accelerate clinical trial recruitment, leading to faster drug development and improved patient access to potentially life-saving treatments, while also addressing fairness concerns in patient selection.