Center for Artificial Intelligence in Medicine & Imaging

AIMI NextGen Tech Talks

AIMI NextGen Tech Talks is an engaging live webinar series tailored for high school students interested in exploring the dynamic intersection of AI in medicine and health. This series provides an opportunity for attendees to gain valuable insights from distinguished experts in the field as they share their impactful professional journeys in shaping the future of healthcare through technology. Webinar participants will have the chance to actively engage in a live Q&A session during the webinar, fostering direct interaction with the speakers.

Tech Talk Episode 4: Dr. Jessica Mega, MD, MPH

Date: Tuesday, June 25th, 2024, at 12:00-1:00pm Pacific Time
Format: Live webinar presentation and Q&A
Registration: Free and open to all ages

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Speaker: 

Jessica L. Mega, MD, MPH is a leader at the intersection of technology, life science, and health care. She is a Cardiologist at Stanford and serves on the Advisory Board for Stanford's Center for Digital Health. She is a Co-Founder of Alphabet's Verily, and she is on the Board of Directors at Boston Scientific and Danaher Corporation, as well as the Board of Advisors for Research!America and the Duke-Margolis Center for Health Policy.

As a faculty member at Harvard Medical School, a Senior Investigator with the TIMI Study Group, and a Cardiologist at Brigham and Women’s Hospital, Dr. Mega led large, international, randomized trials evaluating novel therapies. She also directed the TIMI Study Group’s Genomics Program, demonstrating and testing the role of CYP2C19 genetic variants on antiplatelet medications, a key pharmacogenetic finding. She has published manuscripts in the New England Journal of Medicine, Lancet, and JAMA. She served as an Advisor for the California Governor’s Precision Medicine Initiative.

Dr. Mega is a graduate of Stanford University, Yale University School of Medicine, and Harvard School of Public Health. She completed Internal Medicine Residency at Brigham and Women’s Hospital and Cardiovascular Fellowship at Massachusetts General Hospital. She is board certified in Internal Medicine and Cardiology. She has won the Laennec Society, Samuel A. Levine, and Douglas P. Zipes Awards, and she is a Fellow of the American Heart Association (AHA) and the American College of Cardiology (ACC).
 

Tech Talk Episode 3: Machine Learning in Medical Imaging

Date: Monday, April 29th, 2024, at 5:00-5:45pm Pacific Time
Format: Live webinar presentation and Q&A
Registration: Free and open to all ages

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Speakers:

Speaker Bios:

Dr. Akshay Chaudhari is an Assistant Professor in the Integrative Biomedical Imaging Informatics at Stanford (IBIIS) section in the Department of Radiology and (by courtesy) in the Department of Biomedical Data Science. He leads the Machine Intelligence in Medical Imaging research group at Stanford and has a primary research interest that lies at the intersection of artificial intelligence and medical imaging. His group develops new techniques for accelerated MRI acquisition and downstream image analysis, extracting prognostic insights from already-acquired CT imaging, and developing new multi-modal deep learning algorithms for healthcare that leverage computer vision, natural language, and medical records. Dr. Chaudhari has won the W.S. Moore Young Investigator Award and the Junior Fellow Award from the International Society for Magnetic Resonance in Medicine. Dr. Chaudhari has also been inducted into the Academy of Radiology’s Council of Early Career Investigators in Imaging program. He also serves as the Associate Director of Research and Education at the Stanford AIMI Center and is an advisory board member of the Precision Health and Integrated Diagnostics Center.

Dr. Sergios Gatidis completed his medical training at the University of Tuebingen / Germany and received his Diploma in Mathematics from from the Universities of Tuebingen and Hagen / Germany. His research is focused on multiparametric oncologic medical imaging including hybrid imaging as well as on methods and applications of machine learning for medical image analysis.

Tech Talk Episode 2:  AI in Emergency Medicine

Date: Monday, February 26, 2024, at 5:00-5:45pm Pacific Time
Format: Live webinar presentation and Q&A
Registration: Free and open to all ages

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Speakers:

  • Maame Yaa A. B. Yiadom, MD; Principal Investigator, The Emergency Care Health Services Research Data Coordinating Center (HSR-DCC); Co-Lead, Emergency Department Data Analytics and Prioritization Committee (EDAC); Associate Professor, Emergency Medicine, Stanford University
  • Gabrielle Bunney, MD, MBA, Senior Innovation and Design Fellow, Department of Emergency Medicine at Stanford
  • Rana Ahmad Kabeer, MD, Innovation and Design Fellow, Department of Emergency Medicine at Stanford

Speaker Bios:

Dr. Maya Yiadom is a physician-scientist and leader in emergency care with expertise in clinical operations. Currently, as the Principal Investigator for the Emergency Care Health Services Research Data Coordinating Center (HSR-DCC) and an Associate Professor at Stanford University, she seamlessly integrates academic rigor and industry innovation to tackle hospital challenges. With a healthcare policy background from Princeton and experience as a healthcare management consultant in New York City, Dr. Yiadom completed medical school at Robert Wood Johnson, earned an MPH at Harvard, and pursued health policy reform education at Johns Hopkins. Her research focuses on addressing disparities in time-sensitive care access, especially in cardiovascular disease. As the  Vice Chair for Research in the Stanford Department of Emergency Medicine, she successfully elevated NIH funding, doubled peer-reviewed productivity, and tripled faculty engagement in research. Externally, she serves on the boards of the Emergency Department Benchmarking Alliance and the American Heart Association's Precision Medicine Platform.

Dr. Gabrielle Bunney is an Innovation fellow in the department of Emergency Medicine at Stanford. She has a passion for using artificial intelligence (AI) models to support emergency medicine care delivery and efficiency. She has worked on projects using machine learning models to predict early seizures after intracerebral hemorrhage and identify patients for a hospital’s geriatric intervention program aimed to avoid hospital admission. Her current research projects are focused on designing a model to select patients efficiently and equitably for an early electrocardiogram to detect myocardial infarction. Additionally, she holds an MBA with a focus in finance and is working with groups at Stanford that are bridging the gap between academic medicine and industry. She works on the Fair, Useful Research Models with Monetary value (FURM) evaluation process that examines AI implementation and is a part of the Stanford Emergency Medicine Partnership Program (STEPP) aimed at building collaborations between the ED and companies focused on patient care solutions. The combination of a business background and research skills allow her to focus on the implementation of AI technologies into practice. 

Dr. Rana Kabeer is a first-year fellow in Innovation and Design within the Stanford Department of Emergency Medicine and current MBA-candidate at the Berkeley Haas School of Business. He was born and raised in Michigan, where he completed his undergraduate studies in Human Biology and Michigan State University and went on to obtain a Masters of Public Health degree in International Health Epidemiology from the University of Michigan. He then served as a Substance Abuse Epidemiologist for the State of Michigan Department of Health and Human services before completing his medical degree at the University of Michigan. Throughout his residency at Stanford University, he has been the recipient of multiple educational and clinical awards and served as chief resident in his final year. His research interests include developing novel methods of educational engagement for physicians, exploring methods of teaching innovation-based principles for physicians, and maximizing equity and care delivery related to acute coronary syndrome management within the Emergency Department as part of the Yiadom lab. In his free time, Rana enjoys traveling, going to the movies, and spending time with his partner Carlie and their dog Roxie.

Tech Talk Episode 1:  AI in Medicine

Join us for the launch of Stanford AIMI's exciting new webinar series tailored towards high school students interested in the exciting field of AI in medicine and health. Dr. Curt Langlotz, Director of the AIMI Center, will share about his professional journey and the future of AI in medicine. There will also be a Q&A session with Dr. Langlotz, moderated by Taylor Tam and Sarah Pan, current high school students and former AIMI Center research interns. We will also share details about our 2024 summer research internship program for high school students. Hope to see you there!

Date: Monday, November 13, 2023, at 5:00-5:45pm Pacific Time
Format: Live webinar presentation and Q&A
Registration: Free and open to all ages

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Speaker Bio

Curtis Langlotz, MD, PhD, is a distinguished Professor of Radiology, Medicine, and Biomedical Data Science at Stanford University. Leading a groundbreaking research laboratory, he focuses on utilizing deep neural networks and machine learning technologies to enhance disease detection and reduce diagnostic errors by analyzing medical images and clinical notes. Dr. Langlotz also holds key roles as the Associate Director of Stanford’s Institute for Human-Centered Artificial Intelligence and serves as the Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center). In these capacities, he oversees interdisciplinary artificial intelligence research involving over 150 Stanford faculty members, optimizing the utilization of clinical data to advance healthcare. Additionally, as Associate Chair for Information Systems and a Medical Informatics Director for Stanford Health Care, he oversees the computer technology supporting Stanford Radiology, including an extensive database of 8 million imaging studies.

With a rich academic background from Stanford University, including degrees in Human Biology, Computer Science, Medicine, and Medical Information Science, Dr. Langlotz has made significant contributions to the field. He has authored over 150 scholarly articles and a notable book, "The Radiology Report: A Guide to Thoughtful Communication for Radiologists and Other Medical Professionals." Dr. Langlotz has played pivotal roles in developing industry standards, such as RadLex™ terminology, and has received prestigious awards like the Lee B. Lusted Research Prize and the Career Achievement Award for his outstanding contributions to medical decision-making research and healthcare services. He has also founded healthcare information technology companies, exemplifying his commitment to advancing the healthcare industry through innovation and expertise.