Deep-learning model predicts how fruit flies form, cell by cell
The approach could apply to more complex tissues and organs, helping researchers to identify early signs of disease.
National Institutes of Health (NIH)
New AI system could accelerate clinical research
By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.
Read MoreMachine-learning tool gives doctors a more detailed 3D picture of fetal health
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.
Read MoreResearchers glimpse the inner workings of protein language models
A new approach can reveal the features AI models use to predict proteins that might make good drug or vaccine targets.
Read MoreUsing generative AI, researchers design compounds that can kill drug-resistant bacteria
The team used two different AI approaches to design novel antibiotics, including one that showed promise against MRSA.
Read MoreHow to more efficiently study complex treatment interactions
A new approach for testing multiple treatment combinations at once could help scientists develop drugs for cancer or genetic disorders.
Read MoreWith AI, researchers predict the location of virtually any protein within a human cell
Trained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.
Read MoreAt the 2025 MIT Energy Conference, energy leaders from around the world discussed how to make green technologies competitive with fossil fuels.
Read MoreA fast and flexible approach to help doctors annotate medical scans
“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.
Read MoreNew open-source tool helps to detangle the brain
The software tool NeuroTrALE is designed to quickly and efficiently process large amounts of brain imaging data semi-automatically.
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