Stanford researchers have developed an innovative AI-driven solution that leverages the BERT-based AI model to automatically classify patient-provider messages into 12 distinct categories, reducing clinician workload and enhancing workflow efficiency in healthcare settings.
Researchers at Stanford University have developed a multilayered immiscible polymer system capable of autonomously realigning its layers to enhance the healing process after damage.
Stanford researchers have designed a remote digital health platform to assist diagnosis and management of some inflammatory skin conditions, such as eczema.
Stanford researchers have developed a patient classification method (healthy, idiopathic, diabetic, etc.) based on a quantitative assessment score derived from autonomic and gastric electrocardiogram (ECG) and electrogastrogram (EGG) data.
Actigraphy, or the non-invasive study of human activity-rest cycles, is a field of study of growing importance as ambulatory and at-home monitoring of patients becomes more popular.
Stanford researchers developed a novel flexible smart bandage capable of delivering precise electrical stimulation as part of an early response to wound infections.
Stanford researchers have designed and prototyped an inexpensive, compact and easy-to-use smartphone lens mount for the capture of high quality photographs and videos of the eye's front and back structures.
Stanford researchers in the Kanan Lab have patented a low-cost, portable, and easy-to-use device designed to rapidly detect elevated ammonia levels from a drop of blood.
Stanford inventors have developed TrueImage, a machine learning algorithm to assess the quality of patient images sent in for telemedicine appointments.
Stanford researchers have developed an algorithm using deep learning architectures to predict cardiac function (ejection fraction) and trace the endocardium of the left ventricle from ultrasound echocardiogram videos.