Researchers at Stanford have developed a unified, AI-augmented governance platform purpose-built for the clinical, research, and educational operations of academic medical centers (AMCs).
Stanford scientists have developed and validated a visual scoring system that enables clinicians to reliably assess skin frailty and identify patients at higher risk for complications and skin cancer.
Stanford researchers have developed a machine learning-based application that standardizes patient radiology reporting in a local and secure manner and outperforms all other general large language models (LLMs).
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.
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.
This software tool takes clinical notes from veterinary electronic medical records and assigns SNOMED-CT VET extension diagnostic codes based on the content written on the notes.