About the Course
The University of Chicago’s six-week, live online AI in Health Care: Strategy, Governance, and Transformation course helps health care leaders make decisions that affect care delivery, patient safety, and organizational risk. Participants examine AI’s capabilities and limitations, test vendor claims against clinical needs, prioritize use cases, and determine who should oversee adoption. They also evaluate build, buy, and partner options and consider how to earn clinician trust during implementation.
Across the six weeks, participants build a proposal for an AI initiative at their organization. They develop a clinical and technical risk assessment, governance plan, strategic rationale, and implementation and measurement plan, then share the completed proposal for peer and instructor feedback.
After completing the course, you will be able to:
Evaluate the capabilities, limitations, and emerging applications of AI against specific health care use cases and their clinical risk.
Differentiate AI opportunities that enable enterprise transformation from those that constitute incremental automation.
Assess the governance, organizational, ethical, regulatory, privacy, security, liability, and equity factors that bear on a health care AI initiative, including the internal resources and expertise required to oversee it.
Apply strategy, vendor, and partnership frameworks to evaluate an AI initiative and recommend a build, buy, or partner approach.
Develop a plan for responsible AI implementation that addresses adoption, change management, measurement, workforce needs, and organizational AI literacy.
Participants who successfully complete the course will receive a Certificate of Completion from the University of Chicago, along with a digital credential that can be shared across professional platforms.
Career Outlook
1,033.27
billonThe projected global AI in health care market size by 2034.
Source: Fortune Business Insights
75
%of health systems have at least one AI solution deployed in 2026, up 27% from the previous year.
Source: Agentman
62
%The average global wage premium for workers with AI skills in 2026, up from 57% in 2025.
Source: PwC
Curriculum
Module 1: State of the Industry: The Current AI Landscape in Health Care
Focus: Understand where AI adoption in health care actually stands, and what past technology cycles predict about this one.
Key Topics: History of machine learning and AI in health care; instructive analogs from past technology transitions including EHRs and genomics; cross-industry lessons from finance, manufacturing, and retail; current high-value use cases; the hype cycle and where promises diverge from results; core vocabulary and mental models for non-technical leaders
Module 2: Under the Hood: What AI Is and Is Not, for Clinical Risk
Focus: Build enough technical fluency to test a vendor’s claims and judge whether an approach fits the clinical risk involved.
Key Topics: AI, machine learning, deep learning, and large language models distinguished; how LLMs work, intuition without equations; hallucination, bias, data dependency, and brittleness; what AI cannot do — reasoning gaps, causality, accountability; emerging capabilities including multimodal AI, agents, and reasoning models; testing vendor claims against technical reality; the questions to ask before commitment
Module 3: Governance, Oversight, and Risk
Focus: Decide who owns AI accountability in your organization and build the oversight to support it.
Key Topics: Centralized, federated, and hybrid governance models; Chief AI Officer versus embedding AI in existing functions; IT versus clinical ownership; cross-functional AI committees including clinical, legal, compliance, and patient and community voice; the internal resources and expertise a health system needs to oversee AI; privacy and HIPAA in the age of LLMs; cybersecurity risks specific to AI systems; FDA, CMS, state law, and emerging federal frameworks; liability, ethics, bias, and equity in clinical AI
Module 4: Strategy, Prioritization, and Partnerships
Focus: Prioritize AI initiatives against organizational goals, and decide whether to build, buy, or partner.
Key Topics: What a real AI strategy looks like versus a vision statement; problem-driven prioritization across back office, revenue cycle, clinical operations, and patient experience; roadmaps with milestones and dependencies; communicating to boards, clinical staff, and community partners; build versus buy versus partner by organizational maturity; evaluating AI vendors beyond the demo; contracts, IP ownership, and health data rights; co-development models, investment and venture approaches, and avoiding lock-in
Module 5: Making It Work: Implementation, Adoption, and Measurement
Focus: Structure a rollout clinicians will adopt, and measure whether it created value.
Key Topics: Why most AI implementations fail and how to prevent it; implementation science applied to AI; human-centered design and co-design with clinicians and staff; change management for AI, addressing fear and building trust; measuring what matters across clinical, operational, financial, equity, and staff-experience dimensions; case deep dive on ambient documentation and lessons from real deployments
Module 6: Looking Forward: Workforce, Literacy, and Leadership
Focus: Prepare the organization for what comes next, and present your capstone.
Key Topics: Emerging AI capabilities and their likely health care applications over the next several years; workforce transformation including augmentation, displacement, and reskilling; building organizational AI literacy at every level; the leader’s role in vision, culture, and sustained organizational learning; capstone presentations and peer feedback
*The curriculum and course topics are subject to change.
Applied Project
Participants will build a health care AI proposal across all six weeks, applying the concepts from each module to a real initiative at their own organization.
Week by week, they develop a technical and clinical risk assessment, a governance and oversight plan, a strategic rationale with a build, buy, or partner recommendation, and an implementation and measurement plan. In the final week, these components come together in an integrated proposal, which participants will share with the cohort for feedback from peers and faculty.
Participants without an active AI initiative may develop their proposal using a provided case.
Meet Your Instructors
Samuel L. Volchenboum, MD, PhD, MS
Associate Dean of Master’s Education
Professor of Pediatrics
Director, Data for the Common Good
Section Chief, Section of Biomedical Informatics
Associate Director, Institute for Translational Medicine
Associate Chief Research Informatics Officer
Dr. Sam Volchenboum is the Associate Dean of Master’s Education in the Biological Sciences Division at the University of Chicago, where he is also a Professor of Pediatrics, Associate Chief Research Informatics Officer for the Division of Biological Sciences, Section Chief of Biomedical Informatics, and Program Director of the Clinical Informatics Fellowship. He designed and launched the University of Chicago’s Master’s in Biomedical Informatics, one of the programs he now oversees. He is the founder and Director of Data for the Common Good (D4CG), a research group that builds shared data infrastructure for childhood cancer and rare disease research. Under his direction, D4CG operates a growing portfolio of research data commons spanning pediatric oncology, epilepsy, inflammatory bowel disease, and translational medicine, including the Pediatric Cancer Data Commons. His group also created GEARBOx, a tool that matches children with cancer to open clinical trials. A pediatric hematologist/oncologist, he continues to care for children with cancer and blood disorders.
Dr. Volchenboum received his MD and his PhD in Molecular Biology from the Mayo Clinic. He performed his residency in Pediatrics at Cincinnati Children’s Hospital Medical Center and completed his subspecialty fellowship in Pediatric Hematology/Oncology at Boston Children’s Hospital and the Dana-Farber Cancer Institute. After his fellowship, he pursued a fellowship in Clinical Informatics, earning a Master’s in Biomedical Informatics from the Massachusetts Institute of Technology and training at Harvard Medical School.
His research focus is the design of centralized and federated data infrastructures that lets researchers pool clinical and genomic data across institutions and borders while protecting patients and honoring the terms under which the data were collected. Much of this work grew out of pediatric cancer, where small patient numbers scattered across hundreds of hospitals make shared data a precondition for progress, and it has since extended to rare diseases where the same problem holds. He directs the informatics core for the University of Chicago’s Clinical and Translational Science Award, and he works closely with consortia, foundations, and industry partners to sustain this infrastructure and move it from single studies toward durable resources the field can rely on.
Stephen Weber, MD, ScM
Professor, Section of Infectious Diseases & Global Health
Associate Vice Chair for Faculty Development, Department of Medicine
Director of Leadership and Executive Programming, BSD Office of Master’s Education
Dr. Stephen Weber is the Director of Leadership and Executive Programming for Master’s Education in the Biological Sciences at the University of Chicago, where he is also the Associate Vice Chair for Faculty Development in the Department of Medicine. Dr. Weber served for fourteen years in executive health system leadership, most recently as Executive Vice President and Chief Medical Officer for the University of Chicago Medicine. In this capacity, he led multiple programs and priorities including physician quality and practice, population health, care coordination, informatics, simulation and innovation. He is Co-Founder of the University of Chicago Medicine Center for Digital Transformation and the Center for Healthcare Delivery Science and Innovation. A Professor of Medicine in the Section of Infectious Diseases and Global Health, he continues to see patients in both the inpatient and ambulatory settings.
Dr. Weber received his MD from the New York University School of Medicine and performed his residency in Internal Medicine at the University of Chicago, where he also served as Chief Medical Resident. He completed his subspecialty fellowship in Infectious Diseases at the Beth Israel Deaconess Medical Center in Boston, where he also received a Master’s Degree in Epidemiology from the Harvard School of Public Health.
Dr. Weber’s original research focus was the prevention and management of infection caused by multi-drug resistant organisms among hospitalized patients. He later examined public policy and regulation related to infection control and served for a decade as the Chair of Public Policy and Government Affairs for the Society for Healthcare Epidemiology of America. He’s been influential in shaping national and regional policy and practice around the reporting of healthcare associated infections.
Dr. Weber has been recognized for his outstanding teaching by the University of Chicago Medicine Residency Program and is a two-time recipient of the Department of Medicine’s Outstanding Clinical Service Award. He has been listed among Chicago’s Top Doctor’s in Chicago Magazine for multiple years and has been recognized multiple times by Becker’s Healthcare as one of the top CMOs in the country and a leading expert in Patient Safety.
*UChicago instructors are subject to change.
Do you have questions?
The University of Chicago Approach to Online Learning
The University of Chicago brings rigorous thinking and evidence-based decision-making to professional learning.
AI in Health Care: Strategy, Governance, and Transformation combines live online learning, practical frameworks, health care applications, and applied project work to support responsible AI decision-making. Participants examine how to evaluate opportunities, manage risk, establish governance, and guide adoption across health care organizations.
