Accelerating Generative AI from Concept to Care
Generative artificial intelligence has progressed from experimental language models to clinically oriented systems capable of synthesizing multimodal data, drafting documentation, and informing complex clinical decisions. But how do we convert that algorithmic promise into sustained bedside benefit?
In this one‑hour academic forum, senior leaders from Digital Medicine Society, Kaiser Permanente, Mayo Clinic, and Vanderbilt University Medical Center discussed evidence thresholds, data‑engineering prerequisites, and rapid‑cycle evaluation techniques that permit GenAI to scale without compromising patient safety, equity, or rigor.
Grounded in recent National Academy of Medicine and New England Journal of Medicine reports, the session will offer comparative case analyses and close with a succinct set of system‑level recommendations for healthcare organizations.
Learning Objectives
Grounded in the insights from recent National Academy of Medicine and New England Journal of Medicine reports, attendees will learn to:
- Identify data and infrastructure prerequisites for scalable GenAI deployment.
- Design agile pilot pathways that preserve safety and speed.
- Integrate GenAI tools into clinician workflows to reduce burden and improve care quality.
- Select metrics that demonstrate clinical, operational, and equity impact.
- Avoid common pitfalls revealed by real-world case studies at Digital Medicine Society, Kaiser Permanente, Mayo Clinic, and Vanderbilt University Medical Center.
Who Should Watch
- Health system executives and service line leaders
- Chief medical information officers and chief digital officers
- Clinical informatics professionals and data scientists
- Operational leaders responsible for AI deployment
- Start-ups bringing GenAI solutions to healthcare
- Payers, regulators and policymakers interested in AI assurance
Key References
These landmark publications, authored by today’s panelists and their institutions, provide the evidence base and real‑world lessons that will anchor our discussion.
Generative AI in Health Care—Opportunities, Risks, Responsibilities
National Academy of Medicine Special Publication (2025)
The Burden of Reviewing LLM‑Generated Content
NEJM AI Perspective (January 2025)
Generative Artificial Intelligence in Health and Medicine: Opportunities and Responsibilities for Transformative Innovation
National Academy of Medicine (NAM) (2025)
Quality Assurance During Rapid Implementation of an AI-Assisted Clinical Documentation Tool
NEJM AI Case Study (March 2025)
Meet the Speakers
Matthew R. Callstrom, M.D., Ph.D., is Chair of the Department of Radiology at Mayo Clinic and a member of the Mayo Clinic Board of Governors and Board of Trustees. He also serves as Medical Director for both the Strategy Department and the Generative Artificial Intelligence program, and as Associate Medical Director in the Department of Development. He holds the academic rank of Professor of Radiology and joined the Mayo Clinic staff in 2000.
A physician-scientist, Dr. Callstrom is internationally recognized for his work in image-guided interventions to treat cancer, particularly tumor ablation. His research has helped develop technologies for more precise identification and treatment of tumors in the liver, kidney, bone, lung, and soft tissue. He has led and collaborated on numerous clinical trials, supported by funding from federal agencies, foundations, and industry sponsors.
Dr. Callstrom is a Mayo Clinic Distinguished Clinician and holds full faculty privileges in Clinical and Translational Science at the Mayo Clinic Graduate School of Biomedical Sciences. He is a dedicated mentor and educator and has contributed extensively to the training of future physician-scientists.
He is an active member of several professional societies, including the American Roentgen Ray Society, the Radiological Society of North America, and the Society of Interventional Oncology, where he has served as president. Dr. Callstrom earned his B.S. and Ph.D. from the University of Minnesota, Twin Cities, and completed his M.D., radiology residency, and fellowship at Mayo Clinic College of Medicine and Science.
Peter J. Embí, M.D., M.S., FACP, FACMI, FIAHSI, is a nationally recognized physician-scientist and leader in biomedical informatics. His work focuses on advancing EHR-enabled research, real-world evidence generation, and data-driven learning health systems. He is a pioneer in clinical research informatics and is credited with creating the nation’s first Chief Research Information Officer (CRIO) role.
Dr. Embí is also a leading voice in the responsible use of artificial intelligence in healthcare, having coined the concept of algorithmovigilance to ensure the ethical and equitable deployment of AI in clinical practice. He has led major academic informatics programs, including serving as Chair of Biomedical Informatics and Senior Vice President for Research and Innovation at Vanderbilt University Medical Center (VUMC), and as President and CEO of the Regenstrief Institute.
He currently serves as Professor of Biomedical Informatics and Medicine at VUMC, co-director of both the ADVANCE AI Center and the RAPID-Learning Health System Center, and co-chair of the AI Technologies Governance Committee.
Dr. Embí is a Member of the National Academy of Medicine and a Fellow of several professional societies. He has held national leadership roles with AMIA, ACMI, the NIH, and AHRQ.
Jennifer C. Goldsack, MBA, OLY, is the founder and CEO of the Digital Medicine Society (DiMe), a 501(c)(3) non-profit dedicated to advancing digital medicine to optimize human health. Her work focuses on practical approaches to the safe, effective, and equitable use of digital technologies to improve health, healthcare, and health research.
Jennifer serves on the boards of the Coalition for Health AI (CHAI) and Sage Bionetworks. She is a member of the National Academies of Sciences, Engineering, and Medicine’s Roundtable on Genomics and Precision Health, serves on the World Economic Forum’s Digital Health Action Collaborative, and is an Executive Committee Member of the U.S. Department of Health and Human Services’ National Committee on Vital and Health Statistics (NCVHS).
She earned a master’s degree in chemistry from the University of Oxford, a master’s in the history and sociology of medicine from the University of Pennsylvania, and an MBA from George Washington University.
Vincent Liu, MD, MSc, is the Chief Data Officer for The Permanente Medical Group/Kaiser Permanente and a pulmonary and critical care physician at the Kaiser Permanente Santa Clara Medical Center. He also serves as a Senior Research Scientist at the KP Division of Research.
Dr. Liu leads enterprise-wide programs focused on leveraging advanced analytics, data science, and the responsible use of artificial intelligence to improve care delivery and patient outcomes for more than 4.5 million members across Northern California. He has authored over 200 peer-reviewed publications and is widely recognized for his expertise in complex electronic health record (EHR) data and health system evaluation, particularly in the areas of acute care and sepsis.
Dr. Liu has served as an expert panelist on artificial intelligence and sepsis for national organizations including the National Institutes of Health (NIH), the National Academy of Medicine (NAM), the National Quality Forum (NQF), and the National Committee for Quality Assurance (NCQA). His work bridges clinical practice, research, and technology innovation to drive meaningful improvements in health outcomes.
Lauren Rost, Ph.D., is a Senior AI/ML Engineer who leads the AI Translation Advisory Council at Mayo Clinic, facilitating internal consultation and advocating for AI best practices throughout the AI lifecycle.
Lauren has contributed to Mayo Clinic’s governance structures by providing AI subject matter expertise to support the critical evaluation of internal and external digital health technologies. She has also contributed to AI clinician competency work, as well as metadata management.
Lauren completed her doctoral studies in biomedical informatics at the University of Pittsburgh, where she worked on electronic health record mining, machine learning, and prescribing patterns for both antibiotics and antidepressants.
Lauren is also a member of the American Medical Informatics Association, where she frequently presents and publishes on how we conduct safe, effective, and ethical AI tool development and deployment within Mayo Clinic.
Shauna Overgaard, Ph.D. advances the safe, effective, and equitable integration of artificial intelligence (AI) in healthcare by developing enterprise frameworks for evaluation, implementation, and oversight. Her work bridges innovation with regulatory science, implementation science, and clinical usability to ensure AI meets the highest standards of patient care.
She serves as Senior Director of AI Strategy & Frameworks at Mayo Clinic and Co-Director of its AI Validation & Stewardship Program. Nationally, she co-chairs the National Academy of Medicine’s AI Code of Conduct Working Group for Health Systems and Payers and leads the Coalition for Health AI’s Transparency Working Group, translating ethical and governance principles into actionable guidance. Within the American Medical Informatics Association (AMIA), she co-chairs the AI Evaluation Showcase and contributes to the Public Policy Committee and Industry Partnership Council.
Her scholarly work focuses on translational AI governance, clinical assurance, and equitable adoption. She serves on the Editorial Board of npj Health Systems and as guest editor for BMJ Health & Care Informatics and Mayo Clinic Proceedings: Digital Health. Dr. Overgaard was named a Rising Star in Modern Healthcare’s 2025 Leading Women in Healthcare.
Earlier in her career, she conducted clinical research on diagnostic test development using graph theory and multimodal data in neuroimaging, proteomics, and genomics. She continues to support national consensus-building efforts in medical informatics and health AI.
About the Mayo Clinic Berg Innovation Exchange
Rooted in the Mayo Model of Care and driven by a commitment to patient-centered innovation, the Mayo Clinic Berg Innovation Exchange brings together the world’s brightest minds to surface emerging ideas, confront complex challenges, and chart new paths for the future of human health.