Standard decision models are often resisted by clinical practitioners. This resistance can be well justified: Standard decision models can be opaque and complex, featuring overwhelming calculations, yet at the same time being simplistic, unable to handle the ill-defined structures or lack of information that mark medical settings. Fast-and-frugal heuristics are intuitive models of decision making that use few, obtainable pieces of information and combine them in simple ways. More specifically, fast-and-frugal heuristics rely on simple arithmetic and logic, such as summing some—say, no more than five—variables (e.g., the most important possible side effects of a medical treatment) and ordering these variables (e.g., judging which side effects are more important for most patients). One family of these heuristics that has been applied widely and with success to clinical practice is fast-and-frugal trees. This review uses examples to define, discuss, and show how to build fast-and-frugal trees, while providing literature pointers. The review also features two applications of fast-and-frugal trees for supporting decisions in fetal monitoring and assignment to intensive care. Finally, future theory and applications are discussed, with an emphasis on connections and challenges to building accurate and transparent AI for clinical decision making.