Chief Health AI is building the discipline of evidence-based AI for high-stakes decisions in healthcare and life sciences. We help organizations move from hype to defensible action through structured evidence assessment, deployment planning, and monitoring plans.
Ryan Luginbuhl, MD
Founder & CEO
Ryan is a physician-executive and product leader focused on evidence-based AI for high-stakes decisions in healthcare and life sciences. He was a North Star lead for the COVID-19 Healthcare Coalition, helping mobilize 1,100+ private-sector partners, and led large-scale efforts convening EHR and real-world data collaborators during COVID-19 to generate actionable evidence. He has led health IT modernization work (including Indian Health Service modernization) and built ML/RWD programs bridging research, implementation, and operational reality. He has also led the development of high-performance diabetes prediction models using EHR data in collaboration with a coalition of large self-insured employers. Ryan has advised senior health leaders on the use of AI with EHR data (including NIH leadership) and has presented at the National Academies on sustainable AI for medical and public health preparedness and response. He is a member of the inaugural Stanford Medicine Health Futurists cohort and convenes the quarterly Stanford Medicine Health Futurist Forum (breakout group). He currently teaches HCHE 336: Evidence-Based AI in Life Sciences and Healthcare at Morehouse College and founded Chief Health AI to build the missing discipline between AI demos and AI decisions: translating evidence into testable claims, evaluation plans, and monitoring signals teams can operationalize.
Public Benefit Commitment
Chief Health AI is a Public Benefit Company. We contribute to the field by:
Teaching
Morehouse College course on Evidence-Based AI in Life Sciences and Healthcare, training the next generation in evidence discipline
Convening
Academic-industry roundtables that bring researchers and deployers together
Sharing methods
Publishing practical frameworks for evaluating high-stakes AI responsibly
We believe evidence-based judgment about AI is a teachable, repeatable skill—and that the field needs more practitioners who can bridge research, regulation, and real-world deployment.
Morehouse College: Evidence-Based AI in Life Sciences and Healthcare (HCHE 336)
This undergraduate course teaches students to apply the EvidenceCycle framework to real AI deployment questions. Students build EvidenceAtlases on topics ranging from clinical decision support to AI-augmented research, creating a growing corpus of evidence-based analysis.
Interested in a guest lecture or academic-industry roundtable?Interested in having us teach Evidence-Based AI in Life Sciences and Healthcare at your institution or company?Why “Chief Health”?
We're named for the decision-makers—chief medical information officers, chief data officers, VPs of innovation—who face high-stakes AI choices with limited evidence and enormous pressure to move fast. Our job is to give them the evidence discipline, deployment plans, and monitoring plans they need to act confidently.