Reported June 23 in Nature Biomedical Engineering, researchers at Vanderbilt Health and centers in Hong Kong have created a versatile uncertainty-aware AI framework broadly adaptable as a wrapper for digital pathology AI systems. (An AI wrapper acts as an interface layer that customizes, formats and automates how users interact with the underlying intelligence.) They demonstrate their wrapper, called TRUECAM, primarily with reference to non-small cell lung cancer (NSCLC) subtyping using whole-slide images.

A new study from the Icahn School of Medicine at Mount Sinai shows that social determinants of health—including environmental conditions, health behaviors, access to resources, and social well-being—can play an equally important or even greater role than genetics in predicting a person’s risk of developing common diseases. Published in the June 22 online issue of The American Journal of Human Genetics [DOI: 10.1016/j.ajhg.2026.05.014], the study, titled "Integrating Social Determinants of Health and Genetic Risk in Disease Risk Models," examined how inherited genetic risk and social, behavioral, and environmental factors interact to influence disease risk across diverse populations.