Johns Hopkins University researchers developed MIGHT, a novel AI method optimizing sensitivity and specificity in early cancer screening. Unlike typical accuracy-focused algorithms, MIGHT balances detection trade-offs tailored for large-scale cancer screening contexts, addressing the challenge of low disease prevalence. Utilizing thousands of decision trees, the method refines clinical utility by ensuring high true-positive rates with minimal false positives. This advance represents a significant leap in reliable cancer diagnostics through AI integration.