Evaluating the robustness of an AI pathfinder application on eligibility criteria in multiple myeloma trials using real-world data and historical trials.

Eligibility criteria are pivotal in achieving clinical trial success, enabling targeted patient enrollment while ensuring the trial safety. However, overly restrictive criteria hinder enrollment and study result generalizability. Broadening eligibility criteria enhances the trial inclusivity, diversity and enrollment pace. Liu  proposed an AI pathfinder method leveraging real-world data to broaden criteria without compromising efficacy and safety outcomes, demonstrating promise in non-small cell lung cancer trials. To assess the robustness of the methodology, considering diverse qualities of real-world data and to promote its application. We revised the AI pathfinder method, applied it to relapsed and refractory multiple myeloma trials and compared it using two real-world data sources. We modified the assessment and considered a bootstrap confidence interval of the AI pathfinder to enhance the decision robustness. Our findings confirmed the AI pathfinder's potential in identifying certain eligibility criteria, in other words, prior complications and laboratory tests for relaxation or removal. However, a robust quantitative assessment, accounting for trial variability and real-world data quality, is crucial for confident decision-making and prioritizing safety alongside efficacy.