The engineering Digital Twin provides a framework for combining real world data streams, data analytics, and mechanistic modeling to generate actionable insights. In the same way that the Digital Twin operates on a population of specific asset instances, Personalized Medicine seeks to use detailed physiological data to tailor diagnosis, medical treatment, and the management of health over time for an individual. However, human physiology is a complex, high dimensional, strongly coupled, multi-scale system where many layers of data in the system are inaccessible. Considering these challenges, what are the opportunities and pitfalls as we pursue the Human Digital Twin? In this presentation, we will examine a successful application from medical devices for personalized, closed-loop patient management. We will also look ahead to the critical role of explainable AI and mechanistic modeling for the future of Personalized Medicine and the Human Digital Twin.