Autonomous Machine Learning (AutoML) involves the automation of core ML modules in a deployment and production environment. This talk presents RoboClinic, an innovative AutoML tool for clinical predictions, forecasts, and dashboards. Compared to ClosedLoop (the only existing Auto-ML tool for healthcare), RoboClinic also includes Auto-Business Intelligence (i.e., automatic creation of dashboards), creation of "hot" and "cold" ML sub-profiles during the automation, and ensemble methods of feature selection and algorithm selection. These features create much potential for RoboClinic's application to the healthcare community. RoboClinic has been tested on two local hospitals regarding Length of Stay predictions and mortality predictions of in-patients. The talk will detail RoboClinic's architecture and will discuss the current results, along with the process of its application to clinical data streams.