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BackgroundThere is a growing enthusiasm for machine learning (ML) among academics and health care practitioners. Despite the transformative potential of ML-based applications for patient care, their uptake and implementation in health care organizations are sporadic. Numerous challenges currently impede or delay the widespread implementation of ML in clinical practice, and limited knowledge is available regarding how these challenges have been addressed. ObjectiveThis work aimed to (1) examine the characteristics of ML-based applications and the implementation process in clinical practice, using the Consolidated Framework for Implementation Research (CFIR) for theoretical guidance and (2) synthesize the strategies adopted by health care organizations to foster successful implementation of ML. MethodsA systematic literature review was conducted based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The search was conducted in PubMed, Scopus, and Web of Science over a 10-year period (2013-2023). The search strategy was built around 4 blocks of keywords (artificial intelligence, implementation, health care, and study type). Only empirical studies documenting the implementation of ML applications in clinical settings were considered. The implementation process was investigated using a thematic analysis and coding procedure. ResultsThirty-four studies were selected for data synthesis. Selected papers were relatively recent, with only 9% (3/34) of records published before 2019. ML-based applications were implemented mostly within hospitals (29/34, 85%). In terms of clinical workflow, ML-based applications supported mostly prognosis (20/34, 59%) and diagnosis (10/34, 29%). The implementation efforts were analyzed using CFIR domains. As for the inner setting domain, access to knowledge and information (12/34, 35%), information technology infrastructure (11/34, 32%), and organizational culture (9/34, 26%) were among the most observed dimensions influencing the success of implementation. As for the ML innovation itself, factors deemed relevant were its design (15/34, 44%), the relative advantage with respect to existing clinical practice (14/34, 41%), and perceived complexity (14/34, 41%). As for the other domains (ie, processes, roles, and outer setting), stakeholder engagement (12/34, 35%), reflecting and evaluating practices (11/34, 32%), and the presence of implementation leaders (9/34, 26%) were the main factors identified as important. ConclusionsThis review sheds some light on the factors that are relevant and that should be accounted for in the implementation process of ML-based applications in health care. While the relevance of ML-specific dimensions, like trust, emerges clearly across several implementation domains, the evidence from this review highlighted that relevant implementation factors are not necessarily specific for ML but rather transversal for digital health technologies. More research is needed to further clarify the factors that are relevant to implementing ML-based applications at the organizational level and to support their uptake within health care organizations. Trial RegistrationPROSPERO 403873; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=403873 International Registered Report Identifier (IRRID)RR2-10.2196/47971