AI-Enabled Tools Revolutionize Clinical Trial Feasibility, Moving it Upstream in Protocol Development
Artificial Intelligence (AI)-enabled tools are fundamentally changing how clinical trial feasibility is conducted, shifting it from a retrospective validation step to an ongoing, iterative part of protocol development. Traditionally, feasibility studies were performed late in the process, after a protocol was largely finalized, leading to delays and costly amendments if assumptions about cohort sizes, outcome rates, or eligibility criteria proved incorrect. AI tools, such as those offered by TriNetX, now allow study teams to evaluate assumptions against real-world data (RWD) continuously as criteria are being set. This is made possible by natural language interfaces that enable users to enter eligibility criteria in plain language, which the system then translates into clinical code sets to generate patient counts instantly. For example, adjusting a single BMI exclusion criterion in a type 2 diabetes query can change the cohort size by thousands of patients in real-time, providing immediate visual feedback...