Can AI-Powered TrialGPT Enhance Patient Recruitment for Clinical Trials? | AI in Precision Oncology
Clinical trials are pivotal for medical advancements, yet recruiting participants remains a significant challenge due to complex eligibility criteria and various barriers. Traditional manual screening is time-consuming and prone to errors, often causing delays. TrialGPT, an artificial intelligence (AI)-based system leveraging large language models, offers a promising solution to streamline this process. It comprises three modules: “Retrieval, Matching, and Ranking,” significantly improving the efficiency and accuracy of patient-trial matching. TrialGPT reduces the trial pool by over 90%, matches patient eligibility with 87.3% accuracy, and enhances trial prioritization by 43.8%. Its implementation can expedite recruitment, crucial for time-sensitive research areas, such as oncology. However, challenges such as reliance on proprietary large language models, data privacy, and integration with real-world data persist. As AI technologies continue to advance, TrialGPT exemplifies the potent
