Johns Hopkins University scientists have developed Multidimensional Informed Generalized Hypothesis Testing (MIGHT), an AI-based framework that optimizes cancer screening tests by balancing high sensitivity and specificity. Unlike traditional machine learning models, MIGHT utilizes an ensemble of decision trees to validate diagnostic thresholds robustly, significantly improving early cancer detection accuracy. The study, published in PNAS, showcases potential for more reliable and clinically useful oncology screening tools.