RE: Use of artificial intelligence for cancer clinical trial enrollment

Abstract

Clinical trials represent a pivotal step in advancing novel cancer therapies from development to clinical application.Despite a strong willingness among patients to participate, however, less than 5% of adult patients with cancer are enrolled in oncology trials.Enhancing enrollment workflows can expedite treatment advances and lead to faster patient outcome improvements (1).In their review in this issue of the Journal, Chow et al. ( 2) found that artificial intelligence (AI) workflows for trial enrollment outperformed manual methods, with industry-developed systems having higher positive predictive values than in-house systems.Although we commend Chow et al. for their comprehensive review, we believe that certain concerns warrant further discussion.First, the AI workflows examined had substantial heterogeneities, which complicates the meta-analysis and interpretation of AI performance.AI enrollment workflows can generally be broken down into 3 steps: 1) extracting eligibility criteria from protocols, 2) extracting data from electronic health records, and 3) matching the extracted data with the eligibility criteria (3).The studies included in the review differed greatly in how they approached these steps.For instance, where Meystre et al. (3) applied AI only to the latter 2 steps, Beck et al. (4) automated all 3. Thus, the accuracy of Meystre et al.'s workflow would be less affected by AI than that of Beck et al.Meanwhile, other studies, such as Calaprice-Whitty et al. ( 5), incorporated auxiliary systems such as optical character recognition, which introduced additional failure points that could reduce the study's accuracy.

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