MIT scientists investigate memorization risk in the age of clinical AI
New research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.
New research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.
By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.
Read MoreProfessor Caroline Uhler discusses her work at the Schmidt Center, thorny problems in math, and the ongoing quest to understand some of the most complex interactions in biology.
Read MoreA new approach for testing multiple treatment combinations at once could help scientists develop drugs for cancer or genetic disorders.
Read MoreCellLENS reveals hidden patterns in cell behavior within tissues, offering deeper insights into cell heterogeneity — vital for advancing cancer immunotherapy.
Read MoreResearchers redesign a compact RNA-guided enzyme from bacteria, making it an efficient editor of human DNA.
Read MoreTrained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.
Read MoreThe programmable proteins are compact, modular, and can be directed to modify DNA in human cells.
Read MoreBy sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.
Read More“ScribblePrompt” is an interactive AI framework that can efficiently highlight anatomical structures across different medical scans, assisting medical workers to delineate regions of interest and abnormalities.
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