The use of algorithms in clinical care demands a very high performance level for accurate detection and classification of disease. Deep learning (DL) offers a powerful toolkit necessary to handle the complex variations present in medical data, which traditional statistical or machine learning approaches have historically been unable to capture. In this talk, I will describe the challenges and approaches for the development of high-performance DL algorithms and curation of datasets for problems in diagnostic radiology and cardiology. I will also discuss the use of these algorithms as diagnostic support tools for clinicians, and challenges for the potential translation of these algorithms from the lab setting to clinical practice.
Pranav Rajpurkar is a 4th year Ph.D. candidate in the Stanford Machine Learning Group co-advised by Andrew Ng and Percy Liang. He is interested in engineering artificial intelligence systems for medical applications and exploring how expert decision-making can be assisted with AI decision-support tools. He has developed several deep learning algorithms and datasets for machine diagnosis of medical images, including automated interpretation of X-Rays, ECGs, and MRIs. His research has been covered by popular media outlets including NPR, Wired, and MIT Technology Review. Prior to pursuing his Ph.D., he received his B.S. from Stanford in Computer Science with distinction and honors in 2015 and his M.S. also from Stanford in 2018.