MuscleRehab is a system that uses electrical impedance tomography and optical motion tracking for visualizing muscle engagement and motion data during unsupervised physical rehabilitation.
By continuously monitoring a patient’s gait speed, an in-home wireless system can assess the condition’s severity between visits to the doctor’s office.
An MIT-developed device with the appearance of a Wi-Fi router uses a neural network to discern the presence and severity of one of the fastest-growing neurological diseases in the world.
The MIT researcher and former professor discusses how Covid-19 and the influx of virtual technologies created a new medical ecosystem that needs more synchronized oversight.
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.
Explore the latest machine learning strategies used in a variety of healthcare applications, including the diagnosis of breast cancer, heart arrhythmias, and skin cancer.
Researchers demonstrated that medical AI systems can easily learn to recognize racial identity in medical images, and that this capability is extremely difficult to isolate or mitigate.