In a related editorial, R. Jeffrey Westcott, MD, and James E. Tcheng, MD, said Zack and colleagues' findings support the idea that machine learning could outperform classical statistical approaches to risk prediction--but it'll take some work to make it an industry standard. "Transforming healthcare, and, more specifically, transforming the management of data within healthcare to enable AI and its siblings, requires foundational investment and culture change," the editorialists wrote. They said artificial intelligence and machine learning will undoubtedly become "increasingly important in clinical medicine" as we move forward, with equity funding for healthcare-related AI ventures topping $2.4 billion in 2018. "Machine learning has proven to be valuable and is therefore the future," Westcott and Tcheng wrote. "Data warehouses and data lakes contain amazing amounts of structured and unstructured data that will change how medical research, drug and device trials, and device tracking are done. A collaborative effort is needed with EHR vendors, third-party vendors, professional societies and others to start meaningful standardized data collection and workflow redesign now."