A Qureight innovation spotlight lays out how synthetic control arms use external datasets to compare treatment effects, aiming to reduce dependence on traditional placebo-controlled designs. The piece describes external control arm generation—drawing repeatedly from external datasets—and grouping approaches by whether they use real data with statistical methods or machine learning to produce computer-generated control cohorts. The reporting notes challenges in classic trials such as recruitment and withdrawal and cites use cases where placebo controls are harder to power efficiently. For investigators evaluating efficacy in complex diseases such as idiopathic pulmonary fibrosis, synthetic control arms are presented as a way to improve feasibility and potentially reduce ethical friction around placebo allocation.