Randomized Clinical Trials of Machine Learning Interventions in Health Care

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Abstract
Machine learning has the potential to improve the diagnosis and prognosis of disease to enhance clinical care. Given the increasing amount of digital data generated from routine medical care, available computational processing power, and research advances, such as deep learning, there has been substantial interest in applying machine learning techniques to improve patient care across medical disciplines.1,2 Models have been investigated for tasks such as improved cancer diagnosis, emergency department triage, and intensive care unit decision support.3-5 However, the recent failures to successfully implement machine learning systems in clinical settings have highlighted the limitations of this technology, generating disillusionment and distrust in their potential to impact medicine.6,7 These machine learning system failures are often attributable to a lack of generalizability, an inability to adapt a system trained with data from 1 context to perform well in a new one,8 or an inability to demonstrate a clinically meaningful benefit.7 Mitigation strategies have been proposed to ensure their applicability, such as the use of larger and more diverse data sets and direct collaborations with clinical experts in model development.9-11 We investigated a different and complementary area of study of machine learning model-testing procedures, randomized clinical trials (RCTs), which may affect their ultimate use in heterogeneous clinical settings.

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