Quality standards for artificial intelligence-based studies and clinical trials

Abstract

3][4][5][6][7] To raise the quality of clinical AI prediction modelling studies in the cardiovascular health domain and thereby improve their impact and relevance, the editors for digital health, innovation, and quality standards of the European Heart Journal propose five minimal quality criteria for AI-based prediction model development and validation studies: complete reporting, carefully defined intended use of the model, rigorous validation, sufficient sample size, and openness of data and software.][10][11] In a State of the Art Review article entitled 'Endpoint adjudication in cardiovascular clinical trials', Muhammad Shahzeb Khan from Duke University School of Medicine in Durham, NC, USA, and colleagues note that endpoint adjudication (EA) is a common feature of contemporary randomized controlled trials (RCTs) in cardiovascular medicine. 12EA refers to a process wherein a group of expert reviewers, known as the clinical endpoint committee (CEC), verify potential endpoints identified by site investigators.Events that are determined by the CEC to meet pre-specified trial definitions are then utilized for analysis.The rationale behind the use of EA is that it may lessen the potential misclassification of clinical events, thereby reducing statistical noise and bias.However, it has been questioned whether this is universally true, especially given that EA significantly increases the time, effort, and resources required to conduct a trial.The authors lay out a framework to determine which trials may warrant EA and where it may be redundant.The value of EA is likely to be greater when cardiovascular trials have nuanced primary endpoints, such as myocardial infarction, bleeding, worsening heart failure as an outpatient, unstable angina, or transient ischaemic attack, endpoint definitions that align poorly with practice, suboptimal data completeness, greater operator

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