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Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>While machine learning (ML)-based solutions\u2014often referred to as artificial intelligence (AI) solutions\u2014have demonstrated comparable or superior performance to human experts across various healthcare applications, their vulnerability to perturbations and stability to variations due to new environments\u2014essentially, their robustness\u2014remains ambiguous and often overlooked. In this review, we aimed to identify the types of robustness addressed in the literature for ML models in healthcare. A total of 274 eligible records were retrieved from PubMed, Web of Science, IEEE Xplore, and additional sources. Eight general concepts of robustness emerged. Furthermore, an analysis of those concepts across types of data and types of predictive models revealed that the concepts were differently addressed. 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