AIMI Grand Rounds
The AIMI Grand Rounds, sponsored by the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), is a new series launch held on every fourth Tuesday of the month that is a crucial initiative for disseminating the latest AI advancements in medicine, aiming to drive transformative innovations in healthcare. It provides healthcare professionals and learners with up-to-date, evidence-based, and transformative knowledge necessary for enhancing clinical decision making and healthcare delivery with AI. This series offers interdisciplinary lectures from renowned AI experts across medicine, engineering, and other fields, sharing cutting-edge research, clinical best practices, and other critical considerations related to AI implementation in healthcare. Participants will gain knowledge and tools to apply AI effectively in their practice, fostering innovation and excellence in patient care, and setting new standards in clinical excellence.
Upcoming Grand Rounds
Date: Tuesday, October 28, 2025
Time: 8:00-9:00am PT
Format: Live webinar presentation and Q&A
Registration: Open to the Stanford and AIMI affiliate community
Register Here
Speaker:
Shreya Shah, MD: Clinical Associate Professor of Medicine, Stanford University
Title: Evaluating Generative AI Implementations in Healthcare
About: Shreya Shah, MD, FACP is a physician leader in healthcare informatics, board certified in clinical informatics and internal medicine. She is a clinician, educator, and researcher, with special interests in artificial intelligence and health IT usability. As a Medical Informatics Director of Primary Care and Population Health for Stanford Medicine, she leads the design, implementation and optimization of health information technology in support of clinicians and patients at Stanford. She is also an Associate Medical Director of the Stanford Healthcare AI Research Team, also known as the "HEA3RT" team, whose vision is to be a global leader in the implementation, evaluation, and teaching of AI in health and health care.
Date: Tuesday, September 23, 2025
Time: 8:00-9:00am PT
Format: Live webinar presentation and Q&A
Registration: Open to the Stanford and AIMI affiliate community
Register Here
Speaker:
Lei Xing, PhD: Jacob Haimson & Sarah S Donaldson Professor, Stanford University
Title: Deep and Wide Learning for Enhanced Data-Driven Decision-Making
About: Dr. Lei Xing is the Jacob Haimson & Sarah S. Donaldson Professor and Director of Medical Physics Division of Departments of Radiation Oncology and Electrical Engineering (by courtesy) at Stanford University. He obtained his PhD from the Johns Hopkins University. His research is focused on AI in medicine, data science, medical imaging, and clinical decision-making. Dr. Xing is an author on more than 450 publications in high impact journals, an inventor/co-inventor on many issued and pending patents. He is a fellow of AAPM, ASTRO, and AIMBE. He is the recipient of the 2019 Google Faculty Research Award, and 2023 Edith Quimby Lifetime Achievement Award of AAPM, which denotes outstanding scientific achievements in medical physics, influence on the professional development of others, and organizational leadership.
Date: Tuesday, August 26, 2025
Time: 8:00-9:00am PT
Format: Live webinar presentation and Q&A
Registration: Open to the Stanford and AIMI affiliate community
Register Here
Speaker:
Ivana Maric, PhD: Assistant Professor of Pediatrics, Stanford University
Title: AI for Prediction and Profiling of Maternal and Neonatal Pregnancy Outcomes
About: Ivana Maric is an Assistant Professor in the Pediatrics Department at the Stanford University. Her research focuses on applying machine learning and AI to improving maternal and neonatal health. Her main focus has been on developing models for early prediction of pregnancy outcomes that could guide development of low-cost, point of care diagnostic tools applicable globally and especially in low-resource settings. In recognition of her work in this area, she was awarded the Rosenkranz Prize by the Freeman Spogli Institute for International Studies and Stanford Health Policy at Stanford University. She is also a co-recipient of the IEEE Communications Society Best Tutorial Paper Award.
Date: Tuesday, July 22, 2025
Time: 8:00-9:00am PT
Format: Live webinar presentation and Q&A
Registration: Open to the Stanford and AIMI affiliate community
Register Here
Speaker:
Roxana Daneshjou, MD, PhD: Assistant Professor of Biomedical Data Science and of Dermatology, Stanford University
Title: TBA
About: Dr. Daneshjou studied Bioengineering at Rice University before matriculating to Stanford School of Medicine where she completed her MD and a PhD in Genetics with Dr. Russ Altman as part of the medical scientist training program. She completed dermatology residency at Stanford as part of the research track and completed a postdoc in Biomedical Data Science with Dr. James Zou. She currently is the assistant director of the Center of Excellence for Precision Heath & Pharmacogenomics, director of informatics for the Stanford Skin Innovation and Interventional Research Group (SIIRG), a founding member of the Translational AI in Dermatology (TRAIND) group, and a faculty affiliate of Human-centered Artificial Intelligence (HAI) and the AI in Medicine and Imaging (AIMI) centers.
Date: Tuesday, June 24, 2025
Time: 8:00-9:00am PT
Format: Live webinar presentation and Q&A
Registration: Open to the Stanford and AIMI affiliate community
Register Here
Speaker:
Geoff Sonn, MD: Associate Professor of Urology, Stanford University
Title: The Opportunity for AI to Improve Prostate Cancer Detection and Treatment
About: Geoffrey Sonn is an Associate Professor of Urology and, by courtesy, of radiology. He specializes in treating patients with prostate and kidney cancer. He has a particular interest in cancer imaging, MRI-Ultrasound fusion targeted prostate biopsy, prostate cancer focal therapy, and robotic surgery for prostate and kidney cancer. He was the Stanford principal investigator of a major clinical trial using MRI-guided focused ultrasound to treat prostate cancer. The goal of this trial was to treat prostate cancer with fewer side effects than surgery or radiation. His research focuses on application of deep learning to improve diagnosis and treatment of prostate cancer.
Date: Tuesday, May 27, 2025
Time: 8:00-9:00am PT
Format: Live webinar presentation and Q&A
Registration: Open to the Stanford and AIMI affiliate community
Register Here
Speaker:
Kilian Pohl, PhD: Professor of Psychiatry & Behavioral Sciences and, by courtesy, Electrical Engineering, Stanford University
Title: Crafting Machine Learning Models for Neuroscience Discovery
About: Dr. Pohl is a Professor of Psychiatry and Behavioral Sciences and, by courtesy, of Electrical Engineering, and the Director of the Computational Neuroscience Laboratory (CNSLab) at Stanford University. The focus of his laboratory is to advance computational neuroscience in identifying biomedical phenotypes that enhance personalized medicine toward the diagnosis and prevention of psychiatric disorders from childhood to old age. The CNSLab identifies phenotypes by coupling findings from unbiased, machine learning-based searches across highly dimensional biological, cognitive, neuroimaging, and behavioral data with insightful interpretations by Dr. Pohl’s clinical collaborators. Dr. Pohl is the principal investigator on awards from Stanford’s Institute for Human-Centered Artificial Intelligence and the National Institute of Health (NIH). Before joining Stanford, Dr. Pohl received his Ph.D. in computer science from the Massachusetts Institute of Technology and was faculty at Harvard, IBM Research, the University of Pennsylvania, and SRI International.
Date: Tuesday, April 22, 2025
Time: 8:00-9:00am PT
Format: Live webinar presentation and Q&A
Registration: Open to the Stanford and AIMI affiliate community
Register Here
Speaker:
Sophia Wang, MD, MS: Assistant Professor of Ophthalmology, Stanford University
Title: Challenges and Opportunities for AI in Eye Care
About: Dr. Wang is an ophthalmologist specializing in glaucoma and a clinician scientist in the Department of Ophthalmology at Stanford. Her research focuses on developing and evaluating artificial intelligence methods to predict ophthalmic outcomes using electronic health records. Dr Wang's work on developing algorithms to predict glaucoma progression and evaluating the fairness and generalizability of EHR models is funded by the NIH, the American Glaucoma Society, and Research to Prevent Blindness.
Date: Tuesday, March 25, 2025
Time: 8:00-9:00am PT
Format: Live webinar presentation and Q&A
Registration: Open to the Stanford and AIMI affiliate community
Register Here
Speaker:
Angela Aristidou, PhD: Professor, University College London; Faculty Fellow at the Stanford Digital Economy Lab
Title: AI Deployment in Real-World Clinical Settings
About: Professor Angela Aristidou speaks, writes, and advises about the real-life deployment of artificial intelligence tools for public good. Her research spans the contexts of health, higher education, nonprofit, and humanitarian aid, in the UK, United States, Canada, and several Asian countries. Her current work has been honored through a Stanford CASBS Award and a generous UK Research Innovation Award. She specializes in strategy and entrepreneurship at University College London’s School of Management, is a Fellow at the Stanford Digital Economy Lab and the Stanford Institute for Human-Centered AI, and holds degrees from Cambridge and Harvard.
Moderator:
Sneha Shah Jain: MD, MBA: Clinical Assistant Professor of Medicine, Division of Cardiovascular Medicine, Stanford University
About: Sneha S. Jain is a Clinical Assistant Professor of Medicine in the Division of Cardiovascular Medicine. Her research focuses on the development and responsible evaluation of AI tools to augment healthcare delivery and improve patient outcomes. She works with the Stanford Center for Clinical Research and the Data Science Team at Stanford to deploy and prospectively evaluate AI solutions across the healthcare enterprise. Sneha S. Jain received her BS in Economics from Duke University, MD from the Johns Hopkins School of Medicine, and her MBA from Harvard Business School. She completed internal medicine residency at Columbia/NewYork-Presbyterian, and cardiovascular medicine fellowship at Stanford University.
Date: Tuesday, February 25, 2025
Time: 9:00-10:00am PT
Format: Live webinar presentation and Q&A
Registration: Open to the Stanford and AIMI affiliate community
Watch Recording Here
Speaker:
Nigam Shah, MBBS, PhD: Professor of Medicine, and of Biomedical Data Science; Chief Data Scientist, Stanford Healthcare; Associate Dean for Research, School of Medicine; Associate Director, Stanford Center for Biomedical Informatics Research, Stanford University
Title: Responsible AI at Stanford Healthcare
About: Dr. Shah is Professor of Medicine at Stanford University, and Chief Data Scientist for Stanford Health Care. His research is focused on bringing AI into clinical use, safely, ethically and cost-effectively. Dr. Shah is an inventor on eight patents, has authored over 300 scientific publications, and has co-founded three companies. Dr. Shah was inducted into the American College of Medical Informatics (ACMI) in 2015 and the American Society for Clinical Investigation (ASCI) in 2016. He holds an MBBS from Baroda Medical College, India, a PhD from Penn State University and completed postdoctoral training at Stanford University.
Grand Rounds Launch with Dr. Curtis Langlotz
Date: Tuesday, January 28, 2025
Time: 8:00-9:00am PT
Format: Live webinar presentation and Q&A
Registration: Open to the Stanford and AIMI affiliate community
Watch Recording Here
Speaker:
Curtis Langlotz, MD, PhD: Director, Center for Artificial Intelligence in Medicine & Imaging; Professor of Radiology, Medicine, and Biomedical Data Science, and Senior Associate Vice Provost for Research, Stanford University
Title: Developing Clinically Useful AI for Radiology
About: Dr. Langlotz is Professor of Radiology, Medicine, and Biomedical Data Science, and Senior Associate Vice Provost for Research at Stanford University. His NIH-funded laboratory develops machine learning methods to improve the accuracy and efficiency of medical image interpretation. He also serves as Senior Fellow at Stanford’s Institute for Human-Centered Artificial Intelligence and Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center), which supports over 200 faculty at Stanford who pursue interdisciplinary machine learning research to improve clinical care.
CME Credit Information
Each session is 1.0 credits: AMA PRA Category 1 Credits™ (1.00 hours); Non-Physician Participation Credit (1.00 hours). Credit can only be recorded via text during or up to 24 hours after the session. You must attend the live session to claim credit.