Can healthcare show the way forward for scaling AI?

This article is part of a VB Lab Insights series on AI sponsored by Microsoft and Nvidia. Don't miss additional articles in this series providing new industry insights, trends and analysis on how AI is transforming organizations. Scaling artificial intelligence (AI) is tough in any industry. And healthcare ranks among the toughest, thanks to highly complex applications, scattered stakeholder networks, stringent licensing and regulations, data privacy and security -- and the life-and-death nature of the industry. "If you mis-forecast an inventory level because your AI doesn't work, that's not great, but you'll recover," says Peter Durlach, Executive Vice President and Chief Strategy Officer of Nuance Communications, a conversational AI company specializing in healthcare. "If your clinical AI makes a mistake, like missing a cancerous nodule on an X-ray, that can have more serious consequences." Even with the current willingness of many organizations to fund AI initiatives, many healthcare organizations lack the skilled staff, technical know-how and bandwidth to deploy and scale AI into clinical workflows.