Title: Artificial Intelligence in Medical Imaging

Presenter: Curtis Langlotz, Professor of Radiology, Medicine, and Biomedical Data Science and Senior Associate Vice Provost for Research,  Stanford University 

At speaker’s request, the presentation was not recorded 

Abstract: Artificial intelligence (AI) is an incredibly powerful tool for building computer vision systems that support the work of radiologists. Over the last decade, artificial intelligence methods have revolutionized the analysis of digital images, leading to high interest and explosive growth in the use of AI and machine learning methods to analyze clinical images and text. These promising techniques create systems that perform some image interpretation tasks at the level of expert radiologists. Deep learning methods are now being developed for image reconstruction, imaging quality assurance, imaging triage, computer-aided detection, computer-aided classification, and radiology report drafting. The systems have the potential to provide real-time assistance to radiologists and other imaging professionals, thereby reducing diagnostic errors, improving patient outcomes, and reducing costs. We will review the origins of AI and its applications to medical imaging and associated text, define key terms, and show examples of real-world applications that suggest how AI may change the practice of medicine. We will also review key shortcomings and challenges that may limit the application of these new methods.

Bio: Dr. Langlotz is Professor of Radiology, Medicine, and Biomedical Data Science, and Senior Associate Vice Provost for Research at Stanford University. He also serves as Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center), which comprises over 150 faculty at Stanford who conduct interdisciplinary machine learning research to improve clinical care. Dr. Langlotz’s NIH-funded laboratory develops machine learning methods to detect disease and eliminate diagnostic errors. He has led many national and international efforts to improve medical imaging, including the RadLex terminology standard and the Medical Imaging and Data Resource Center (MIDRC), a U.S. national imaging research resource.

Title: Patients and Clinicians at the heart of health innovation: OpenNotes Lab and Cornell Tech Health Tech Hub

Presenter: Chethan Sarabu, Clinical Assistant Professor, Pediatrics, Stanford University

Bio: Chethan Sarabu MD, FAAP, FAMIA, trained in landscape architecture, pediatrics, and clinical informatics, builds bridges across these fields to design healthier environments and systems. He is the inaugural Director of Clinical Innovation for the Health Tech Hub at Cornell Tech’s Jacobs Institute. Over the past six years, Sarabu has been a Clinical Assistant Professor of Pediatrics at Stanford Medicine and has worked in the health tech industry as Head of Product, Director of Clinical Informatics, and Medical Director at doc.ai and later Sharecare. He collaborates with the OpenNotes Lab as an AI and Informatics Strategist and serves as a board member of The Light Collective. 

Title: GWAS in the age of AI  

Presenter: Degui Zhi, Professor and Chair, Department of Bioinformatics and Systems Medicine, UTHealth Houston 

Abstract: While genome-wide association studies (GWAS) have fueled the amazing genetic discovery in the past 15 years or so, most existing studies were using traditional phenotypes. With deep learning-based AI, it is possible to generate many new phenotypes. Powered by big data in biobanks, many new loci can be discovered. As a result, the landscape of GWAS might be different. In this talk, I will discuss a possible future with large-scale AI-driven GWAS. 

Bio: Degui Zhi is Glassell Family professor of biomedical informatics, and founding chair of Department of Bioinformatics and Systems Medicine at the McWilliams School of Biomedical informatics at the University of Texas Health Science Center at Houston (UTHealth Houston). Dr. Zhi is also the founding director of Center for AI and Genome Informatics. He received his PhD in bioinformatics at UC San Diego. Before joining UTHealth, he was a tenured associate professor of statistical genetics at University of Alabama at Birmingham. Dr. Zhi is interested in developing AI deep learning and informatics methods for biomedical big data. His team developed multiple generalist deep learning frameworks for the modeling of biomedical data, including Med-BERT, a clinical foundation model for structured clinical data, gene2vec, a distributed representation embedding model for genes based their co-expression patterns, and unsupervised deep learning models for deriving endophenotypes for genetic discovery. His team also developed advanced PBWT-based data structures and algorithms for population genetics informatics.

Title: Reflections on AI in (NYC) government 

Presenter: Neal Parikh, Director of AI for New York City

Abstract: AI and machine learning have emerged as increasingly ubiquitous technologies in a wide range of areas in both the private sector and in government. In the past several years, ethical and other policy and governance questions around how and whether to use AI for various tasks have become much more prominent, partly due to its widespread use and partly due to publicly documented failures or shortcomings of a number of systems that can negatively impact people in sometimes serious ways. 

Bio: Neal Parikh is a computer scientist who most recently served as Director of AI for New York City. He is currently Adjunct Associate Professor at Columbia University’s School of International & Public Affairs, teaching a new class on AI for policymakers. Previously, he co-founded a technology startup, which was acquired after 10 years in operation, was Inaugural Fellow at the Aspen Tech Policy Hub at the Aspen Institute, and worked as a senior quant at Goldman Sachs. He received his Ph.D. in computer science from Stanford University, focusing on large-scale machine learning and convex optimization; his research has received over 25,000 citations in the literature and is widely used in industry.