The use of machine-learning (ML) algorithms in medicine has sparked a heated discussion. It is considered one of the most disruptive general-purpose technologies in decades. It has already permeated many areas of our daily lives and produced applications that we can no longer do without, such as navigation apps or translation software. However, many people are still unsure if ML algorithms should be used in medicine in their current form. Doctors are doubtful to what extent they can trust the predictions of algorithms. Shortcomings in development and unclear regulatory oversight can lead to bias, inequality, applicability concerns, and nontransparent assessments. Past mistakes, however, have led to a better understanding of what is needed to develop effective models for clinical use. Physicians and clinical researchers must participate in all development phases and understand their pitfalls. In this review, we explain the basic concepts of ML, present examples in the field of thrombosis and hemostasis, discuss common pitfalls, and present a methodological framework that can be used to develop effective algorithms.