Health CARE‑AI framework helps health teams lead AI with ethics and equity

The initiative received support from the Faculty of Medicine’s Artificial Intelligence (AI) Seed Funding Program in 2024, which helped the team build an evidence‑based foundation for the framework.

The AI Seed Funding Program is a uOttawa Faculty of Medicine initiative that offers up to $10,000 for one‑year projects that build AI capacity in health and medicine. It supports new and ongoing projects that move AI into real‑world use and is backed by the AI Knowledge Translation Fund. The program also reflects the Faculty’s commitments to inclusivity, diversity, equity, accessibility and social justice, which are woven into the competition process.

CARE‑AI fits that goal. Led through EqHS, the project starts from a clear observation: AI tools are showing up across professional education, clinical care and health‑system planning. They change how people learn, make decisions and plan services. That brings clear benefits, but it also raises questions about who is accountable, how data are used, and whether AI might deepen existing inequities for patients, communities and learners.

For Dr. Daniel McEwen, Strategic Research, Innovation and Partnerships Lead at EqHS, one core issue is trust. “AI is moving quickly into professional education, clinical practice and the day‑to‑day work of health teams,” says McEwen. “We wanted to understand how we could integrate AI into the systems people depend on in ways that are ethical and worthy of that trust.”

The AI Seed grant gave CARE‑AI a solid base. The goal was to create an evidence‑informed foundation for the framework and position the team for further research collaborations and funding. The seed‑funded phase opened the door to a successful application to the Future Skills Centre, a research and collaboration centre funded by the federal government’s Future Skills Program, which is now supporting broader dissemination, implementation and training.

To develop the framework, the team brought together people in diverse roles, in Canada and internationally, to test what professionalism should mean when AI is part of decision-making. A consensus process helped refine the principles and build shared agreement on what matters most in practice.

The result is the Health CARE‑AI Framework, which brings nine professionalism principles together under four themes: values, competence, accountability and structural equity. The values‑focused principles stress shared responsibility for AI use and AI literacy so people understand both what AI can offer and where it can cause harm. The competence‑related principles keep human judgement at the centre, stress thoughtful interaction with AI tools and call for integrity and transparency about how they are used. The accountability principles highlight legal duties, privacy and consent. The structural equity principles focus on bias reduction and equity in design, so AI systems are built and used in ways that do not worsen existing gaps in care.

“One of the clearest early signs of success has been the exceptional engagement throughout the study,” said Dr. Lyn Sonnenberg, co-principal investigator for CARE-AI. “More than 300 contributors offered nearly 900 substantive, content-rich comments that directly shaped the framework. That depth of participation reflects a strong appetite across the health professions for practical, responsible guidance on AI. The final endorsement was near-unanimous—96% of participants agreed that CARE-AI clearly defines the professionalism expectations needed to meet the educational, technological and ethical demands of AI in health professions. This level of engagement and support signals both the urgency of the work and the field’s readiness to adopt a shared standard.”

Today, CARE-AI has evolved from a set of principles into a package of practical resources that include a step-by-step guide with checklists, decision aids and clear how-to strategies. The Health CARE‑AI Implementation Guide and Toolkit is now undergoing final validation by researchers, educators, administrators and clinicians.

The group is clear that CARE‑AI is a living framework, not a one-and-done effort. For it to make a difference, organizations need to treat AI ethics and equity as part of quality and safety, not add‑ons.

“Long-term success depends on three core conditions: institutional adoption, leadership alignment, and sustained investment in capacity building, said Sonnenberg. “CARE-AI is designed to be practical, but it requires institutions to weave the principles into everyday governance, policy, and quality improvement.”

Ongoing evaluation will be essential, because CARE-AI is intentionally iterative and will require renewal as technologies evolve and new evidence risks and lived experiences emerge.

The CARE‑AI initiative shows what can grow from a modest AI Seed grant: a shared language for AI professionalism, a framework shaped by diverse voices and usable resources. Through the AI Seed Funding Program, the Faculty of Medicine is supporting the development of responsible medical AI capacity across its community. The 2025 round of the AI Seed Funding Program is now closed, with successful projects to be announced in early 2026. The OMARI team looks forward to highlighting more stories like CARE‑AI as funded projects move from ideas to practice. 

To learn more about CARE-AI and the EqHS lab, contact [email protected].