Model replaces the laborious process of annotating massive patient datasets by hand.
Researchers hope the system can zero in on the right patients to enroll in clinical trials, to speed discovery of drug treatments.
When time matters in hospitals, automated system can detect an early biomarker for the potentially life-threatening condition.
Steven Keating SM'12, PhD '16 inspired millions with his research-driven approach to battling cancer and his advocacy for open patient health data.
Dutch delegation visits the Institute for a tour focused on computing, robotics, and health care innovation.
MDaaS Global works to transform health care in Africa by bringing high-end medical diagnostics to low-income communities.
Interacting with a robotic teddy bear invented at MIT boosted young patients’ positive emotions, engagement, and activity level.
System helps machine-learning models glean training information for diagnosing and treating brain conditions.
In some cases, radio frequency signals may be more useful for caregivers than cameras or other data-collection methods.
MIT/MGH's image-based deep learning model can predict breast cancer up to five years in advance.
Projects will develop new AI technologies that detect and prevent diseases.
The one-year Scale-Ups Fellowships program provides each fellow with a $20,000 grant and support tailored to address their specific needs.
The GOSSIS algorithm was developed using data from hospitals around the world to spur greater multi-center collaboration and improved benchmarking.