By Dr. Tania Tajirian, Chief Health Information Officer and Chief of Hospital Medicine, CAMH
Clinical AI adoption in Canada is accelerating, but not evenly.
Across the country, artificial intelligence is being introduced into clinical workflows, from documentation support and imaging to early warning systems and decision support. While momentum is growing, adoption remains fragmented, siloed, and concentrated in certain settings. Visibility into what is deployed, where, and with what impact is limited.
Without shared visibility and common approaches, Canada cannot effectively govern, evaluate, or scale clinical AI in a responsible and equitable way.
This is the challenge the CHIEF Executive Forum’s AI in Action Working Group set out to address when it formed last year.
Until recently, Canada lacked several foundational building blocks for system-level decision-making on clinical AI:
- A pan-Canadian view of deployed clinical AI initiatives
- A common language to compare use cases across jurisdictions and care settings
- A consistent approach to evaluating impact, limiting the ability to inform policy, funding, and scale decisions
In the absence of these elements, organizations have been left to pilot in isolation, often repeating the same work with limited opportunity for shared learning.
Stage 1: Establishing a national baseline
AI in Action began by addressing the visibility gap.
Through a structured, pan-Canadian environmental scan, the initiative produced the first standardized national baseline of clinical AI activity in Canada. Using a consistent protocol across jurisdictions and publicly available sources, the scan captured where and how clinical AI is being used, including care setting, use case, technology type, maturity, and outcomes where available. View the scan here
The results confirmed both momentum and constraint. Canada is active, with more than 150 clinical AI initiatives identified in the original scan, but activity is uneven, early-stage, and rarely supported by consistent outcome reporting.
This fragmentation has consequences. It leads to duplicated effort, uneven access across care settings, weak evidence for scale, and growing challenges for governance and procurement.
The real barrier: the absence of shared evaluation and readiness frameworks
One conclusion from Stage 1 is clear. The constraint is not a lack of innovation or interest in clinical AI. It is a lack of the absence of shared evaluation and readiness frameworks, particularly:
- Semantic, with inconsistent definitions and metrics
- Workflow, with wide variation in how AI is implemented in practice
- Organizational, with limited cross-jurisdiction learning
- Evaluative, with few comparable measures of impact
Without addressing these gaps, scaling clinical AI responsibly will remain slow and uneven.
Stage 2: Moving from mapping to enabling
Stage 2 of AI in Action shifts the focus from understanding the landscape to building the shared evaluation, readiness, and governance foundations needed to act on it.
This phase centres on the development of a practical, pan-Canadian clinical AI evaluation toolkit, designed to be usable across diverse healthcare settings. Work underway includes:
- A minimum viable evaluation pathway, defining a small, standardized set of core measures that any organization can apply, even with limited analytic capacity
- An expanded evaluation pathway for more mature settings, with guidance on outcomes, costs, and workflow impacts to support scale and investment decisions
- Implementation readiness guidance and governance considerations, focused on key clinical, technical, data, and organizational factors that influence responsible deployment
- Practical tools and templates, including metric libraries, ROI input structures, and plain-language guidance to support consistent use
- A structure for national comparability and learning, allowing results from local pilots to be shared, compared, and reused across jurisdictions
Together, these components are intended to reduce duplication, support better evidence generation, and help organizations move beyond isolated pilots toward system-level impact.
From pilots to system impact
Canada does not have a shortage of clinical AI initiatives. What it has lacked is shared structure.
AI in Action is focused on building the common taxonomy, evaluation pathways, and readiness frameworks needed to turn fragmented experimentation into coordinated, equitable, and scalable system impact.
In short: Stage 1 mapped the landscape. Stage 2 is building the tools Canada needs to move forward.
Next steps
Details of completed AI in Action Stage 1 work will be shared with healthcare leaders at the FHLIP Conference (Toronto, February 20, 2026) and e-Health26 (Halifax, June 14–16), as part of a broader national conversation on how Canada can scale clinical AI responsibly.
The AI in Action scan is a living resource. We invite healthcare providers and organizations to share details of their clinical AI initiatives, so the database remains accurate and current. Feedback on inaccuracies or omissions is welcome. Please contact chief@digitalhealthcanada.com. The AI in Action: Transforming Clinical Care Across Canada Working Group will continue building on this work—engaging leaders across Canada, surfacing lessons learned, and supporting responsible AI adoption. Our shared aim is to ensure that AI in healthcare advances in ways that are evidence-based, equitable, and patient-centred.