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2Aifred Health, Inc., Montreal, QC, Canada 3Department of Information Science, Bar-Ilan University, Ramat Gan, Israel 4Department of Mathematics, Queens College (CUNY), New York City, NY, United States Editorial on the Research Topic The increasing performance of machine learning and artificial intelligence (ML/AI) models has led to them being encountered more frequently in daily life, including in clinical medicine (Bruckert et al.; Rosenfeld et al., 2021). While concerns about the opaque “black box” nature of ML/AI tools are not new, the need for practical solutions to the interpretability problem has become more pressing as ML/AI devices move from the laboratory, through regulatory processes that have yet to fully catch up to the state-of-the-art (Benrimoh et al., 2018a), and to the bedside. This special edition targets three key domains in which innovation and clearer best practices are required for the implementation of ML/AI approaches in healthcare: ensuring safety, de

Noord hollandM ali heidelbergM weimer heidelbergClinical decision support system for mental healthResearch topicSpringer international publishingTechnology in applied intelligenceHealth canadaAifred healthComprehensible artificial intelligenceMental healthBuilding intelligentRecent trendsFuture technologyIntraocular lensBig data analytics