Randomized Clinical Trials (RCTs) have had a long, and often illustrious, history in the biomedical and clinical sciences. However, as Deaton and Cartwright make clear, population-based RCTs are assumption-laden and not necessarily the most appropriate or compelling strategies to use to assess an intervention in many settings. For example, the emergence of ‘personalized’ medicine, in which interventions are chosen for an individual patient based on that patient’s nuanced and possibly unique genetic or biochemical profile, has called into question the value of population-based RCTs. Although many researchers have proposed trial designs appropriate for advancing personalized medicine, this commentary focuses on the motivation and basic elements of three extensions and alternatives to traditional RCTs appropriate for personalized medicine. These extensions and alternatives to RCTs leverage modern developments in biomedical assays and health technologies, ‘big data’ collections and artificial intelligence, and include isolated N-of-1 trials, aggregated N-of-1 trials, trial designs that test an algorithm for matching interventions to patient profiles rather than testing a single intervention, and clinical and health ‘rapid learning systems.’ Ultimately, it is argued that although some elements of the traditional RCT are not likely to go away, how these elements are exploited to advance personalized medicine will be radically different than the way they were leveraged in population-based RCTs of the past.