We systematically reviewed studies of implementation science frameworks used for healthcare AI deployment (2020-2026). Following PRISMA 2020, we searched MEDLINE, Embase, Web of Science, and Scopus and included 87 empirical studies. CFIR was most common (42.5%), followed by RE-AIM (28.7%) and EPIS (18.4%). The most frequent barriers were data infrastructure limitations (67.8%), clinician trust deficits (58.6%), and regulatory uncertainty (52.9%). Implementation success was associated with organizational readiness (r=0.64, p<0.001) and leadership engagement (OR=2.34, 95% CI 1.89-2.91). Generative AI deployments showed higher clinician adoption (78.3% vs 62.1%) but required additional governance for reliability and hallucination risk. Overall, successful AI implementation depends on framework-guided planning, active leadership, and long-term governance for sustainability. Full Text Available: Implementation Science for AI Integration in Digital Health Systems
Topol et al. (Thu,) studied this question.