Stanford Webinar - From Pixels to Human Impact: Unlocking Real-World Value with Computer Vision
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/aiLearn more about our Deep Learning for Computer Vision courses: https://online.stanford.edu/courses/xcs231n-deep-learning-computer-vision
https://online.stanford.edu/courses/cs231n-deep-learning-computer-vision
Assistant Professor Ehsan Adeli leads an informative session on how computer vision works and its applications across various domains, particularly in robotics and healthcare. You will hear how innovative computer vision techniques can transform everyday activities and behaviors into meaningful clinical signals.
Professor Adeli explains how computer vision algorithms continuously identify clinical activities and objects in hospitals, enabling automated tracking of preventive measures, diagnostic procedures, and therapeutic interventions essential for patient safety and care quality. By delivering real-time, detailed insights into clinical workflows—traditionally dependent on limited human observation or retrospective records—this system has the potential to reduce clinician workload, improve adherence to care protocols, and ultimately enhance patient outcomes in high-intensity environments such as the ICU.
Additionally, he discusses the role of computer vision in senior care applications, highlighting how it can detect early signs of cognitive or physical decline, equipping clinicians with new tools for proactive and compassionate care. Watch this enlightening session to gain a deeper understanding of how AI is connecting advanced technology with meaningful human outcomes.
About the speaker:
Professor Ehsan Adeli holds a Ph.D. in artificial intelligence and computer vision, with postgraduate training in computational neuroscience. His research group develops Translational AI algorithms for healthcare and mental health, focusing on the automatic analysis of human activities from videos and neuroimages.
Adeli’s work focuses on bridging digital humans and human neuroscience through advanced computer vision and video analysis. His group develops methods to capture and analyze 3D motion and complex behaviors using multi-modal sensing technologies, integrating these with neuroimaging data. This approach not only pushes the boundaries of visual understanding of human behavior but also creates new pathways for connecting computational models with brain function. Receive SMS online on sms24.me
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