So hello, I'm Matt Lungren and I'm a practicing physician scientist and faculty, at Stanford University School of Medicine. In addition to treating patients in my clinical interventional practice, I also lead a large multidisciplinary AI in medicine center at Stanford. And we bring together many dozens of faculty from departments all over the medical school. And campus, to collaborate on development and implementation of new clinical machine learning applications, to solve clinically important problems. So basically that means I get to work every day with brilliant passionate scientists, doctors and students and one of the most exciting areas of science. It's pretty much the coolest job ever, but then again, I'm biased and we'll talk about bias in this course, by the way. >> Hello. I'm Serena Yeung, and I'm faculty in Stanford's department of biomedical data science. And by courtesy, the departments of Computer Science and Electrical Engineering. I lead a research group on medical AI and computer vision, where I work with amazing students and researchers, in coalition collaborators on both the development of AI algorithms, as well as the deployment of these algorithms in hospitals and other healthcare environments. I also teach at Stanford, both the course on AI and healthcare in the School of Medicine, as well as the course on deep learning for computer vision, in the School of Engineering. The lectures for deep learning for computer vision course, have been publicly released and have several million collective views online. >> We're both really excited to present this course to you and we spent a lot of time working to smooth out very complex principles, and try to fill in with real world clinical examples. As well as occasionally throwing lessons learned by ourselves and many others in the field. >> And so by the end of this course, you'll be confidently up to speed on the fundamentals of AI and machine learning and healthcare, so that you can go on to accomplish great things. Whether it's continuing your education into more in depth programs, applying to get your dream job, or making your existing dream job even better, or simply to learn a lot about an exciting topic and have fun along the way. >> Advancements of machine learning and AI into all areas of medicine are now a reality, and they hold the potential to transform healthcare and open up a world of incredible promise for everybody. One of the things that makes this technology so transformative in healthcare, are the practically limitless applications. Everything from automated screening and diagnosis, adaptive clinical trials, operations research, global health, precision medicine, home health and wearables, genomic analysis, drug discovery and design, robotics, and many more. But in order to successfully develop and deploy these systems into the healthcare domain, and realize their full potential benefits, it's going to require everybody to have basic competencies in both healthcare and machine learning concepts and principles. This includes AI developers, tech companies, policymakers and regulators, healthcare system leadership, pharmaceutical and device industry, frontline conditions, ethicists, even patients and patient caregivers. Basically everyone interested in or already a part of the healthcare ecosystem, should be empowered to impact the choices necessary to responsibly and ethically use these new technologies in a way, that benefits everyone.