Health data uses are on the rise. Increasingly more often, data are used for a variety of operational, diagnostic, and technical uses, as in the Internet of Health Things. Never has quality data been more necessary: large data stores now power the most advanced artificial intelligence applications, applications that may enable early diagnosis of chronic diseases and enable personalized medical treatment. These data, both personally identifiable and de-identified, have the potential to dramatically improve the quality, effectiveness, and safety of artificial intelligence. Existing privacy laws do not 1) effectively protect the privacy interests of individuals and 2) provide the flexibility needed to support artificial intelligence applications. This paper identifies some of the key challenges with existing privacy laws, including the ineffectiveness of de-identification and data minimization protocols in practice and issues with notice and consent as they apply to artificial intelligence applications, then proposes an alternative privacy model. This model introduces a more restrictive approach to health privacy while introducing an interest balancing approach to data processing and retention to benefit individuals and the general public.