On Aug. 20, 2025, two studies led by Johns Hopkins Kimmel Cancer Center, Ludwig Center, and Johns Hopkins Whiting School of Engineering researchers report on a powerful new method that significantly improves the reliability and accuracy of artificial intelligence (AI) for many applications. As an example, they apply the new method to early cancer detection from blood samples, known as liquid biopsy.

One study reports on the development of MIGHT (Multidimensional Informed Generalized Hypothesis Testing), an AI  method that the researchers created to meet the high level of confidence needed for AI tools used in clinical decision making.  To illustrate the benefits of MIGHT, they used it to develop a test for early cancer detection using circulating cell-free DNA (ccfDNA)—fragments of DNA circulating in the blood. A companion study found that ccfDNA fragmentation patterns used to detect cancer also appear in patients with autoimmune and vascular diseases. To develop a test with high sensitivity for cancer but reduced false-positive results, MIGHT was expanded to incorporate data from autoimmune and vascular diseases obtained from colleagues at Johns Hopkins and other institutions who treat and study these diseases.

The studies, supported in part by the National Institutes of Health, were published in the Proceedings of the National Academy of Sciences.

A related article, authored by three researchers from Johns Hopkins, Pixar co-founder Ed Catmull, Ph.D., and Microsoft chief data scientist of the AI for Good Lab Juan Lavista Ferres, was published concurrently in Cancer Discovery, a publication of the American Association for Cancer Research.  It discusses the challenges of incorporating AI into clinical practice, including challenges addressed by MIGHT.

MIGHT fine-tunes itself using real data and checks its accuracy on different subsets of the data, using tens of thousands of decision-trees, and can be applied to any field employing big data, ranging from astronomy to zoology. It is particularly effective for the analysis of biomedical datasets with many variables but relatively few patient samples, a common situation in which traditional AI models often falter.