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.
Professors Facundo Batista and Dina Katabi, along with three additional MIT alumni, are honored for their outstanding professional achievement and commitment to service.
MIT CSAIL and McMaster researchers used a generative AI model to reveal how a narrow-spectrum antibiotic attacks disease-causing bacteria, speeding up a process that normally takes years.
By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.
MIT CSAIL researchers developed a tool that can model the shape and movements of fetuses in 3D, potentially assisting doctors in finding abnormalities and making diagnoses.
VaxSeer uses machine learning to predict virus evolution and antigenicity, aiming to make vaccine selection more accurate and less reliant on guesswork.
Tools build on years of research at Lincoln Laboratory to develop a rapid brain health screening capability and may also be applicable to civilian settings such as sporting events and medical offices.
The Language/AI Incubator, an MIT Human Insight Collaborative project, is investigating how AI can improve communications among patients and practitioners.
Launched with a gift from the Biswas Family Foundation, the Biswas Postdoctoral Fellowship Program will support postdocs in health and life sciences.
The MIT-MGB Seed Program, launched with support from Analog Devices Inc., will fund joint research projects that advance technology and clinical research.