TrialGenie: Empowering Clinical Trial Design with Agentic Intelligence and Real World Data

Clinical trial design (CTD) is a time-consuming process that requires substantial domain expertise. Large-scale real-world data (RWD), such as electronic health records (EHR), encodes practice-based evidence that is of tremendous value to CTD. In recent years, many machine learning methods have been developed to extract such real-world evidence (RWE) from the RWD to inform CTD, but they still need to be communicated with the domain experts extensively in an iterative manner to be further refined and ultimately useful. In this paper, we introduce TrialGenie, an agentic framework that derives RWE for helping with CTD. Through the iterative conversation and analysis across agents with different roles, TrialGenie can autonomously refine trial protocols and finally generate a robust report containing insights that inform better CTD. We applied TrialGenie on the CTD process of several acute diseases including septic shock, acute heart failure, acute pulmonary edema, and acute kidney injury using the MIMIC-IV data. The results demonstrate TrialGenie’s capabilities in facilitating and accelerating the CTD process.