Although not always considered a domain of digital health, artificial intelligence (AI) and machine learning (ML) are inseparable, given that virtually all digital health tools utilize advance algorithm technologies. For example, a wearable device is not directly measuring sleep. It is measuring movement, electrical impulses, and other variables that are processed via algorithm and presented as a representation of sleep. Beyond digital devices, AI is being seen as a revolution in health care driven by the hopes that algorithms running on electronic medical record systems will predict when a patient is turning septic or that AI will free clinicians from tedious tasks. Much of this is coming true, but there is also a growing list of potential hazards such as trusting “black boxes,” racial bias, and poor technical execution. This chapter discusses ways to find the right balance between the progress and problems of AI in health care.