In conference rooms and clinics across the country, leaders are asking the same question: How do we move from excitement about artificial intelligence to systems that actually work — safely, responsibly, and at scale?
At the Stanford Division of Computational Medicine, a new program aims to answer that question.
Stanford AI in Healthcare Leadership and Strategy: from Innovation to Implementation is a four-week hybrid experience designed for the people responsible for turning AI ideas into real-world impact. Hosted in collaboration with the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), the Stanford Center for Continuing Medical Education, and the ARISE Network, with support from Stanford Division of Hospital Medicine, the program blends online learning, live virtual discussions, and a two-day in-person immersion at Stanford University.
Why This Program, Now?
Healthcare is drowning in complexity. Administrative burden is rising. Clinical workflows are strained. AI promises relief, but only if it can be trusted.
“The complexity and administrative burden of modern healthcare is overwhelming for patients, clinicians, and the systems that support them, which is why there is so much hope that AI can help,” says Jonathan H. Chen, MD, PhD, Associate Professor in the Division of Computational Medicine and Director for Medical Education in Artificial Intelligence. “But usefulness alone isn’t enough. These systems have to be trustworthy in real-world settings. This program focuses on how to rigorously evaluate and deploy AI that organizations can actually depend on.
That emphasis on rigor is central. Participants learn how to evaluate models, assess readiness, navigate governance, and integrate tools into clinical and operational workflows, all within the messy realities of healthcare systems.
Learning That Extends Beyond the Classroom
For Co-Program Director Ethan Goh, MD, MS, Executive Director of the Stanford ARISE (AI Research and Science Evaluation) Network and a founding editorial board member of BMJ Digital Health & AI, the value of the program goes well beyond content delivery.
Goh explains “This program is designed to create sustained interactions among participants working on similar problems, and direct engagement with leaders across academia, health systems, and industry who have real experience deploying AI in healthcare. Those relationships, and the shared problem-solving that comes with them, are a core part of the program.”
Over four weeks, participants engage with case studies, hands-on workshops, and expert-led discussions. The structure is intentional: asynchronous modules build foundational knowledge, live sessions provide depth and dialogue, and the in-person immersion creates space and trust for deeper collaboration.
Who This Program is For
The program welcomes both clinical, business and technical leaders, including:
- Health system executives and operational leaders guiding AI strategy
- Physicians and nurses integrating AI into care delivery
- Data scientists and informaticians leading digital initiatives
- Product and engineering leaders building AI tools
- Policy and regulatory professionals shaping responsible governance
- Industry leaders in health tech, pharma, and payer organizations
Guest lecturers include C-suite leaders from industry and academia, including experts from OpenAI, Roche, Google, Harvard Medical School, and the FDA, alongside Stanford faculty working at the forefront of AI research and clinical integration. Program participants will learn from enduring healthcare AI leaders like Curt Langlotz, MD, PhD, and Nigam H. Shah, MBBS, PhD, who have been implementing clinical AI for years, as well as frontier lab researchers shaping the next wave of AI in healthcare.
From Strategy to Stewardship
As AI tools become embedded in clinical decision-making, documentation, imaging, operations, and patient engagement, the stakes are high. Decisions made today will shape trust in healthcare technology for years to come. What role should clinical leaders play in healthcare AI development?
This program reflects Stanford Medicine’s broader commitment to advancing responsible, evidence-based AI. It recognizes that leadership in this space requires more than technical fluency. It demands strategic leadership, ethical clarity, and the ability to translate innovation into sustainable impact.
For Chen, the goal is to prepare leaders who can build systems that clinicians and patients can truly rely on.
For Goh, it’s also about community: bringing together people from different industries, united by a passion for healthcare, who are tackling similar challenges and equipping them to solve those problems together.
