Responsible AI for Health Symposium 2026
Held in Washington, D.C., February 24, 2026.
(Hosted by the Center for Digital Health and Artificial Intelligence (CDHAI) at the Carey Business School, the Bloomberg School of Public Health, the Berman Institute of Bioethics, the School of Medicine, and the Whiting School of Engineering.)
This year’s Responsible AI for Health Symposium invited leaders in policy, business, technology, medicine, and academia to come together with the purpose of moving the discussion of AI in health care beyond the abstract debates of the past few years. The focus of these discussions was on how this technology is truly designed, deployed, regulated, and experienced by both users and developers.
Program Materials
Paper 1: FaceAge, a deep learning system to estimate biological age from face photographs to improve prognostication: a model development and validation study
Paper 2: The fragile intelligence of GPT-5 in medicine
Paper 3: Socio-Demographic Modifiers Shape Large Language Models’ Ethical Decisions
Paper 5: Subliminal Learning: Language models transmit behavioral traits via hidden signals in data
Paper 6: Ethical limitations of algorithmic fairness solutions in health care machine learning
Paper 7: Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models
Paper 8: Can an accurate model be bad?
Paper 9: Medical Large Language Model Benchmarks Should Prioritize Construct Validity
Paper 1: FaceAge, a deep learning system to estimate biological age from face photographs to improve prognostication: a model development and validation study
Paper 2: The fragile intelligence of GPT-5 in medicine
Paper 3: Socio-Demographic Modifiers Shape Large Language Models’ Ethical Decisions
Paper 5: Subliminal Learning: Language models transmit behavioral traits via hidden signals in data
Paper 6: Ethical limitations of algorithmic fairness solutions in health care machine learning
Paper 7: Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models
Paper 8: Can an accurate model be bad?
Paper 9: Medical Large Language Model Benchmarks Should Prioritize Construct Validity
Keynote Speakers
Panel: Bridging Academia and Industry in Responsible AI
Exploring how academia and industry can collaborate to develop and deploy responsible, trustworthy AI in healthcare and beyond.
Panel: Perspectives from Patients, Providers, Payors, and the Care Team
A multi-stakeholder discussion examining the role of responsible AI in health care from patient, provider, payor, and care team perspectives.
NEXUS AWARDS
RAIHS is supported in part by the Johns Hopkins Nexus Award. The Nexus Awards, a university-wide initiative launched in 2023, support convening, research, and teaching anchored at Johns Hopkins Bloomberg Center in D.C. at 555 Pennsylvania Ave. These esteemed awards recognize projects and programs that encapsulate the vast range of faculty expertise, from artificial intelligence and health policy to the arts, humanities, global health, and gender equity.