The authors of a new study published in the American Association for the Advancement of Science (AAAS) have found that machine learning enables cheaper and safer low-power magnetic resonance imaging (MRI) without sacrificing accuracy. These advances pave the way for affordable, patient-centric, and deep learning-powered ultra-low-field (ULF) MRI scanners, addressing unmet clinical needs in diverse healthcare settings around the world.