Artificial intelligence (AI) machine learning is a rapidly emerging brain modeling tool for mental health research, psychiatry, neuroscience, genomics, pharmaceuticals, life sciences, and biotechnology. Scientists have identified areas of potential weak spots in AI brain models and offer solutions on how to prevent bias in a new peer-reviewed study. The research team led by Abigail Greene at Yale School of Medicine along with co-authors affiliated with Yale University, Brigham and Women's Hospital, Harvard Medical School, University of Washington, and Columbia University Irving Medical Center's Department of Psychiatry points out the need to identify why AI algorithms for brain models do not work for everyone when seeking to understand brain-phenotype relationships without biases. "Individual differences in brain functional organization track a range of traits, symptoms and behaviors," wrote the scientists. "So far, work modelling linear brain–phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants."