Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
AI uses patient data to optimize selection of eligibility criteria for clinical trials
An artificial-intelligence tool called Trial Pathfinder can run clinical-trial emulations using health-care data from people with cancer, and can learn how to optimize trial-inclusion eligibility criteria, while maintaining patient safety.
Clinical trials are the main way to determine whether new treatments are safe and effective. Trial success can depend on the timely enrolment of a representative sample of individuals who meet the eligibility criteria. However, enrolling enough people to draw a statistically significant conclusion about a trial result can be a problem. Writing in Nature, Liu et al.1 present a software tool that offers a data-driven way to optimize the inclusiveness and safety of eligibility criteria by learning from the real-world clinical data of people with cancer.
National Academies of Sciences, Engineering, and Medicine. Strategies for Ensuring Diversity, Inclusion, and Meaningful Participation in Clinical Trials: Proceedings of a Workshop (National Academies, 2016).