  Perez et al's study using the Apple Watch to identify atrial fibrillation
(AF) is a watershed moment in large-scale machine learning for wearable
computing. Identifying relevant patients will be tremendously important to
research in healthcare. For a condition like AF, this could reduce stroke risk
by two thirds. In the study by Perez et al, only 450 out of 420,000 individuals
had ground truth data. Their study excluded 417,000 participants using the
irregular pulse notification. This design decision means their study was only
able to report positive predictive value (PPV) and unable to explore
sensitivity or specificity. In this editorial, we explore the difficulty of
obtaining ground truth data and its implications for study design.
