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.
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.
Upcoming Grand Rounds
Tuesday, January 27, 2026
8:00am-9:00am PT
RSVP for Webinar
Joseph Liao, MD
Professor of Urology
Stanford University
Title: Enhanced Imaging and Technologies and Image-Guided Surgery for Bladder Cancer
Abstract: Bladder cancer ranks as the sixth most common malignancy, with 75% of the patients presenting at an early stage amenable to endoscopic management. Diagnostic cystoscopy combined with transurethral resection of the bladder tumor (TURBT) remains the standard for diagnosis, treatment, and surveillance. The ability to inspect the interior of the bladder reflects a long history of advances in optical imaging and endoscopy. However, despite these advances, recurrence and progression after endoscopic resection remain substantial, in part due to incomplete resection and provider-related variability. Consequently, unmet clinical needs exist for solutions that improve tumor enumeration, delineation and resection quality. Recent advances in enhanced cystoscopy technologies and artificial intelligence offer promising avenues to address these challenges. This presentation will review the current status and opportunities in realizing AI-assisted cystoscopy and TURBT, including dataset curation, model development and training, system integration, and steps toward clinical translation.
Joseph Liao is the Kathryn Simmons Stamey Professor and Vice Chair of Urology at Stanford University School of Medicine. Dr. Liao is a physician scientist whose primary research interest lies in the development and translation of precision diagnostics for major urological diseases, particularly bladder cancer. He leads a multidisciplinary lab that has advanced endoscopic imaging and image-guided surgery of bladder cancer using fluorescence, endomicroscopy, molecular imaging, and most recently, artificial intelligence. His research is funded by the NIH and Department of Veterans Affairs, and he directs an NIH K12 career development program for physician scientists at Stanford. A graduate of Harvard College, he earned his medical degree from Stanford University and completed his urology residency and fellowship training at UCLA.
Past Grand Rounds
Tuesday, January 28, 2025
8:00am-9:00am PT
Watch Recording Here
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
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.
Tuesday, February 25, 2025
9:00am-10:00am PT
Watch Recording Here
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
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.
Tuesday, March 25, 2025
8:00-9:00am PT
Watch Recording Here
Angela Aristidou, PhD
Professor
University College London
Title: AI Deployment in Real-World Clinical Settings
Abstract: This talk will address the puzzle of AI deployment in real-world clinical settings. First, I will examine the chasm between AI design and AI real-world deployment in clinical and hospital settings. Then I will highlight some key strategies to bridge this chasm. Finally, I will offer evidence from a real-world case of AI deployment in a real-world clinical setting. I focus on a predictive AI tool designed in collaboration between the UK National Health Service and a Big Tech Company to support Radiology teams in outlining organs at risk, and the deployment took place in a UK hospital. This talk will not focus on the technical aspects of the AI tool, but rather it will focus on how to support AI adoption in health organizational settings.
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.
Tuesday, April 22, 2025
8:00-9:00am PT
Watch Recording Here
Sophia Wang, MD, MS
Assistant Professor of Ophthalmology
Stanford University
Title: Challenges and Opportunities for AI in Eye Care
Abstract: This presentation will discuss using electronic health records (EHR) to develop AI algorithms for eye care. By presenting several examples motivated by the desire to improve glaucoma care, the presentation will illustrate some of the challenges in using EHR for developing ophthalmology algorithms, such as those related to aggregating and standardizing eye-relevant data, and algorithm fairness and generalizability. The presentation will also discuss some promising initiatives to study and overcome these challenges, including ongoing initiatives to incorporate eye data into multicenter registries and data standardization efforts.
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.
Tuesday, May 27, 2025
8:00-9:00am PT
Watch Recording Here
Killian Pohl, PhD
Professor of Psychiatry & Behavioral Sciences and, by Courtesy, Electrical Engineering
Stanford University
Title: Crafting Machine Learning Models for Neuroscience Discovery
Abstract: Machine learning has had limited impact on improving the diagnosis and prevention of psychiatric diseases as their findings often fail to generalize beyond the neuroscience data they were trained on. In this talk, I will review the most critical challenges in using machine learning to advance discovery in neuroscience. For example, the presence of confounding effects often results in data-driven inference identifying spurious and biased associations. I will show that traditional approaches are often unsuitable for minimizing their effect on 3D brain MRI studies and propose alternative strategies, such as augmenting training data via synthetic 3D MRI generated by conditional diffusion models. I will review findings of the proposed deep learning approaches on large publicly available data sets (such as ABCD study, > 10K samples) and smaller in-house studies (< 100 samples).
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.
Tuesday, June 24, 2025
8:00-9:00am PT
Watch Recording Here
Geoff Sonn, MD
Associate Professor of Urology
Stanford University
Title: The Opportunity for AI to Improve Prostate Cancer Detection and Treatment
Abstract: To understand the current gaps in prostate cancer diagnosis, To understand the role of prostate cancer focal therapy, To understand how AI may be able to improve prostate cancer diagnosis and treatment.
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.
Tuesday, July 29, 2025
8:00-9:00am PT
Watch Recording Here
Roxana Daneshjou, MD, PhD
Assistant Professor of Dermatology
Stanford University
Title: Skin in the Game: The Impact of AI on Dermatology
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.
Tuesday, August 26, 2025
8:00-9:00am PT
Watch Recording Here
Ivana Maric, PhD
Assistant Professor of Pediatrics
Stanford University
Title: AI for Prediction and Profiling of Maternal and Neonatal Pregnancy Outcomes
Abstract: Every day estimated 800 women and 7000 newborns die from pregnancy-related complications. For many of these complications -- including hypertensive disorders of pregnancy, preterm birth and being born to small – early prediction, prevention and therapy are limited or not known. In this talk, we will present AI methods that can enable early prediction of pregnancies at-risk, identification of biomarkers and better biological profiling of these conditions, and from there, potentially guide early interventions. We will focus on solutions that are applicable worldwide and especially in low-resource settings.
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.
Tuesday, September 23, 2025
8:00-9:00am PT
Watch Recording Here
Lei Xing, PhD
Jacob Haimson & Sarah S Donaldson Professor
Stanford University
Title: Biomedicine in the Age of AI and Foundation Models
Abstract: AI and Foundation Models (FMs) are revolutionizing biomedical research and medicine, offering a critical path to breakthrough discoveries and substantially improved patient care. However, their full potential is hindered by significant challenges related to their training and fine-tuning. Current deep learning models often rely on a brute-force approach that ignores crucial prior system knowledge, leading to extensive data requirements and suboptimal performance. In this talk, I will elaborate on the important role of prior system knowledge in deep learning models and introduce strategies to integrate this information into the data-driven decision-making process. I will demonstrate the efficacy of this novel framework through applications in biomedical imaging and omics data analysis. Integrating prior knowledge substantially improves computational efficiency, enhances interpretability, and reduces the incidence of model hallucinations, accelerating scientific discoveries and advancing personalized medicine.
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.
Tuesday, October 28, 2025
8:00am-9:00am PT
Watch Recording Here
Shreya Shah, MD
Clinical Associate Professor
Stanford University
Title: The Implementation and Evaluation of Generative AI Solutions in Healthcare
Abstract: Generative AI has the potential to enhance healthcare outcomes and patient experience while reducing clinician burden. As healthcare organizations consider integrating these novel AI technologies, it is crucial to ensure appropriate applications that address meaningful healthcare challenges. Rigorous evaluation is essential for any generative AI deployment in clinical practice to understand the potential benefits and challenges, inform iterative improvements for technology developers, and support organizations in scaling these solutions effectively. Our mixed-methods approach to evaluation leverages implementation science and qualitative evaluation frameworks that are adaptable to diverse generative AI applications. We present examples of real-world evaluations of generative AI implementations. Use cases include AI-generated draft replies to patient messages and draft-result comments and to mitigate electronic health record in-basket burden, as well as ambient AI scribes to reduce clinical documentation burden.
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.
Tuesday, November 18, 2025
8:00am-9:00am PT
Watch Recording Here
Jonathan Chen, MD
Associate Professor of Medicine and Biomedical Data Science
Stanford University
Title: AI in Medicine - Real Magic or Illusions
Abstract: Pandora’s box has opened in the form of publicly available generative AI systems for every imaginable (and many unintended) purposes. With a global scarcity of medical expertise against the unlimited demand of people in need, AI's potential to democratize healthcare knowledge, access, and to recover efficiencies is desperately needed. The implications are vast as we converge upon a point in history where human vs. computer generated content can no longer be reliably distinguished. This session will review the attention and intention required for AI applications in the high-stakes world of healthcare as we distinguish real magic from convincing illusions.
Jonathan H. Chen MD, PhD leads a research group to empower individuals with the collective experience of the many, combining human and artificial intelligence approaches to deliver better care than either alone. Dr. Chen continues to practice medicine for the concrete rewards of caring for real people and to inspire this research focused on discovering and distributing the latent knowledge embedded in clinical data.
Tuesday, December 16, 2025
8:00am-9:00am PT
RSVP for Webinar
Eric Yang, MD, PhD
Clinical Professor of Pathology
Stanford University
Title: Applied Computational Pathology
Abstract: Applied computational pathology brings together digital pathology and AI to support and improve routine diagnostic practice. This presentation highlights practical AI use cases across the pre-analytical, analytical, and post-analytical stages of the pathology workflow. It also examines key deployment challenges, including slide digitization, AI model compatibility, and building pathologist confidence in AI outputs. The session aims to provide a concise overview of the current landscape of AI in pathology.
Dr. Yang is a Clinical Professor of Pathology at Stanford University specializing in gynecologic pathology and cytopathology. His NIH-funded computational pathology research program focuses on advancing the field through new imaging technologies and AI-enabled diagnostic tools.