Johns Hopkins University researchers introduced MIGHT, a novel AI framework for cancer screening enhancing sensitivity at a high specificity target of 98%. By employing multidimensional informed generalized hypothesis testing combined with random forest modeling and extensive cross-validation through so-called MIGHTY Trees, this method promises optimal trade-offs in prediction, addressing challenges of traditional machine learning models in low disease prevalence settings and improving early detection capabilities.