Whiterabbit.ai was founded in 2017 by Silicon Valley tech entrepreneur Rakesh Mathur and Stanford trained medical imaging researcher Jason Su. Our founding was deeply rooted in our co-founders personal experience - Rakeshs story...
Santa Clara, California
3930 Freedom Circle
Santa Clara, CA, 95054
Artificial Inte... Health Care Medical Indust... Image Processi... Radiology Breast Cancer ... Cancer Diagnos... Treatment Deep Learning
51 - 200
Whiterabbit.ai was founded in 2017 by Silicon Valley tech entrepreneur Rakesh Mathur and Stanford trained medical imaging researcher Jason Su. Our founding was deeply rooted in our co-founders personal experience - Rakeshs story (https://www.london.edu/lbsr/changemakers-rakesh-mathur) fuels our mission to eliminate suffering for others. Our mission is to significantly reduce late-stage cancers by detecting them in their earliest stages, starting with breast cancer. We are developing a state-of-the-art artificial intelligence (AI) suite to improve: Breast cancer screening rates Radiologists accuracy, productivity, and workflow Clinic operations to ensure patients receive timely and compassionate care The patient experience through a mobile app that gives the consumer access to their medical images with the ability to instantly share Nearly forty million women get mammograms every year in the US. About 1 in 8 women will develop invasive breast cancer over the course of her lifetime. We hope through developing robust AI technologies, we can improve the speed and accuracy of cancer detection in radiology. Our solutions in development are aimed at redesigning the entire breast cancer screening process with a focus on compassion. Through redefining and shortening the screening and diagnosis experience for women, we aim to improve their patient experiences and outcomes. Training AI to solve complex problems requires a robust data set. Whiterabbit and The Mallinckrodt Institute of Radiology at Washington University School of Medicine have forged a collaboration to build a world-class data set and lead the advancement of breast cancer detection with machine learning.