  Precision health, increasingly supported by digital technologies, is a domain
of research that broadens the paradigm of precision medicine, advancing
everyday healthcare. This vision goes hand in hand with the groundbreaking
advent of artificial intelligence (AI), which is reshaping the way we diagnose,
treat, and monitor both clinical subjects and the general population. AI tools
powered by machine learning have shown considerable improvements in a variety
of healthcare domains. In particular, reinforcement learning (RL) holds great
promise for sequential and dynamic problems such as dynamic treatment regimes
and just-in-time adaptive interventions in digital health. In this work, we
discuss the opportunity offered by AI, more specifically RL, to current trends
in healthcare, providing a methodological survey of RL methods in the context
of precision and digital health. Focusing on the area of adaptive
interventions, we expand the methodological survey with illustrative case
studies that used RL in real practice.
  This invited article has undergone anonymous review and is intended as a book
chapter for the volume "Frontiers of Statistics and Data Science" edited by
Subhashis Ghoshal and Anindya Roy for the International Indian Statistical
Association Series on Statistics and Data Science, published by Springer. It
covers the material from a short course titled "Artificial Intelligence in
Precision and Digital Health" taught by the author Bibhas Chakraborty at the
IISA 2022 Conference, December 26-30 2022, at the Indian Institute of Science,
Bengaluru.
