Improved screening using AI

Improved screening using AI



Delays in clinical trial enrollment and difficulties enrolling representative samples continue to vex sponsors, sites, and patient populations. Here we investigated use of an artificial intelligence-powered technology,, as a means of overcoming bottlenecks and potential biases associated with standard patient prescreening processes in an oncology setting.

Methods: was applied retroactively to 2 completed oncology studies (1 breast, 1 lung), and 1 study that failed to enroll (lung), at the Comprehensive Blood and Cancer Center, allowing direct comparison between results achieved using standard prescreening practices and results achieved with Outcome variables included the number of patients identified as potentially eligible and the elapsed time between eligibility and identification.


For each trial that enrolled, use of resulted in a 24% to 50% increase over standard practices in the number of patients correctly identified as potentially eligible. No patients correctly identified by standard practices were missed by For the nonenrolling trial, both approaches failed to identify suitable patients. An average of 19 days for breast and 263 days for lung cancer patients elapsed between actual patient eligibility (based on clinical chart information) and identification when the standard prescreening practice was used. In contrast, ascertainment of potential eligibility using took minutes.


This study suggests that augmentation of human resources with artificial intelligence could yield sizable improvements over standard practices in several aspects of the patient prescreening process, as well as in approaches to feasibility, site selection, and trial selection.
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