Assistant Professor of Radiology and Biomedical Informatics (courtesy) at Stanford University and Associate Director of the Stanford Artificial Intelligence in Medicine and Imaging Center (AIMI). He is Principal Investigator on federally funded work focusing on integrating medical imaging data with clinical data to improve the delivery of medical imaging diagnostics on a global scale. His work is among the first to evaluate delivery of machine learning tools for medical imaging in a clinical setting and leads multi-institutional clinical trials for evaluation of new AI medical imaging tools in the clinical practice setting. He leads dozens of industry collaborations on medical imaging AI that feature co-engineering new applications to transform healthcare delivery. He has published over 75 scientific publications and his work is regularly featured in national news outlets such as The Wall Street Journal, VICE News, and NPR.
One of the most promising areas of health innovation is the application of artificial intelligence, machine learning, and deep learning primarily in medical imaging. The increase in the amount of data and the possibility to use ML/DL to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to more objective science. Radiologists, who were at the forefront of the digital era in medicine, can guide the introduction of AI solutions that accommodate ethical and regulatory requirements into healthcare.