More sensitive X-ray imaging
Improvements in the material that converts X-rays into light, for medical or industrial images, could allow a tenfold signal enhancement.
Improvements in the material that converts X-rays into light, for medical or industrial images, could allow a tenfold signal enhancement.
A model’s ability to generalize is influenced by both the diversity of the data and the way the model is trained, researchers report.
Engineers build a lower-energy chip that can prevent hackers from extracting hidden information from a smart device.
A new deep-learning algorithm trained to optimize doses of propofol to maintain unconsciousness during general anesthesia could augment patient monitoring.
Heather Kulik embraces computer models as “the only way to make a dent” in the vast number of potential materials that could solve important problems.
Assistant Professor Marzyeh Ghassemi explores how hidden biases in medical data could compromise artificial intelligence approaches.
New fellows are working on electronic health record algorithms, remote sensing data related to environmental health, and neural networks for the development of antibiotics.
The machine-learning model could help scientists speed the development of new medicines.
MIT neuroscientists have developed a computer model that can answer that question as well as the human brain.
A new method automatically describes, in natural language, what the individual components of a neural network do.
MIT scientist Rosalind Picard collaborates with clinicians to develop tools for mental health care delivery.
Overseeing business and research units across MIT Open Learning, Breazeal will focus on the future of digital technologies and their applications in education.
Senior research scientist and her team are designing intelligent systems that could someday transform the way we travel and consume energy.
MIT ocean and mechanical engineers are using advances in scientific computing to address the ocean’s many challenges, and seize its opportunities.
Scientists demonstrate that AI-risk models, paired with AI-designed screening policies, can offer significant and equitable improvements to cancer screening.