The digitization of healthcare has accelerated the adoption of machine learning and artificial intelligence applications for clinical care. Access to real-world data from the electronic health record and clinical imaging systems has allowed for the development of novel predictive algorithms. The goal of these algorithms is to deliver on the promise of big data and precision medicine: increased efficiency, tailored therapies, and improved patient outcomes. With the growth in adoption, there has also come concern about the need for regulatory oversight in this rapidly evolving field. This session will explore proposed regulatory frameworks for clinical decision support and artificial intelligence applications in healthcare with use cases demonstrating best practices for the successful implementation of predictive models. Understanding these approaches will allow organizations to adopt these novel technologies while ensuring safe and effective clinical care.