Teaching AI to ask clinical questions
Researchers have made strides toward machine-learning models that can help doctors more efficiently find information in a patient’s health record.
Researchers have made strides toward machine-learning models that can help doctors more efficiently find information in a patient’s health record.
A geometric deep-learning model is faster and more accurate than state-of-the-art computational models, reducing the chances and costs of drug trial failures.
Researchers develop a comfortable, form-fitting fabric that recognizes its wearer’s activities, like walking, running, and jumping.
Piction Health, founded by Susan Conover SM ’15, uses machine learning to help physicians identify and manage skin disease.
Researchers develop tools to help data scientists make the features used in machine-learning models more understandable for end users.
The second AI Policy Forum Symposium convened global stakeholders across sectors to discuss critical policy questions in artificial intelligence.
MIT alumni-founded Overjet analyzes and annotates dental X-rays to help dentists offer more comprehensive care.
A new system lets robots manipulate soft, deformable material into various shapes from visual inputs, which could one day enable better home assistants.
MIT scientists unveil the first open-source simulation engine capable of constructing realistic environments for deployable training and testing of autonomous vehicles.
A new technique in computer vision may enhance our three-dimensional understanding of two-dimensional images.
A new computational model could explain differences in recognizing facial emotions.
The new design is stackable and reconfigurable, for swapping out and building on existing sensors and neural network processors.
Recent MEng graduates reflect on their application-focused research as affiliates of the MIT-IBM Watson AI Lab.
MIT professor will leverage his research into machine learning and computer science, as well as his role as a practicing cardiologist, toward educating clinician-scientists and engineers.
A machine-learning method imagines what a sentence visually looks like, to situate and ground its semantics in the real world, improving translation, like humans can.