Nurses are essential for Canada to achieve AI for All

Canada’s National Artificial Intelligence Strategy: AI for All is a strategic and timely investment in the country’s future. The identification of health and life sciences as a priority sector in AI for All is further reinforced by the targeted multi-billion-dollar investment in the new AI Missions Program to improve “health outcomes for Canadians” (Innovation, Science and Economic Development Canada, 2026). As Canada launches this high-stakes initiative, it should be made clear that sustainable and successful AI for All in healthcare is dependent on partnering with the largest professional group of the healthcare workforce: nurses. 

Canada’s nurses hold unique expertise that is essential to ensuring this technological evolution responds to the needs of patients, providers, and healthcare systems, ultimately improving (and not worsening) health equity, access and outcomes. In every community across the nation, from coast, to coast, to coast, nurses are there: more than 450,000 strong in practice, education, leadership and research. There is no route to the strategy’s health mission, or to its adoption targets in any health setting that does not run through nurses.

Risks of excluding nursing perspectives from AI for All 

Digital health history is riddled with missed opportunities in efficiency, scalability, and sustainability of new health innovations owing to a lack of nursing engagement and co-design (Dykes & Chu, 2020). Given the already accelerated pace, demands, and complexity of AI integration, we need to overcome this frequent omission now (Al Khatib & Ndiaye, 2025). Nursing knowledge can help bridge the gap between the promises and actualized benefits of AI technologies in healthcare (Nature Medicine, 2026; Rossetti et al., 2025). As interdisciplinary experts, nurses coordinate care while balancing competing priorities among patient needs, team capacity, available technology, and system constraints, and also holding the very expertise that determines whether any tool helps or hinders authentic clinical workflows. Early and consistent nursing engagement now can help expand the development of AI tools to better address real-world clinical problems including priorities of patients and communities, beyond the narrow focus on technical novelty that continues to dominate much AI development in health (Nature Medicine, 2026). 

Nursing expertise is also upstream of the strategy’s stated commitments to trust and equity. Pillar 1 warns that “biases built into algorithms can also cause harm to vulnerable communities,” and commits Canada to “the world’s first AI equity based national standard on accessible AI” (Innovation, Science and Economic Development Canada, 2026). But bias in health AI is not only a modelling problem; it originates in the data, and much clinical data originates in nursing documentation. When nursing assessments, interventions, and outcomes are inconsistently captured or inadequately represented, the resulting systems structurally neglect all dimensions of patient care. Under-investment in nursing data and documentation standards is therefore an upstream cause of biased health AI, and nursing informatics capacity is part of the trustworthy, sovereign foundation the current government’s AI strategy intends to build. Pillar 1 also states plainly that citizens “will not adopt technologies they consider dangerous or harmful”. In practice, it is nurses who operationalize that trust at the bedside, and equity assessment must weigh not just model accuracy, but whose data are included and whose experiences are missing, something nursing is ideally positioned to bring voice to.

Opportunities for aligning AI for All strategies with the work of nurses 

Well-designed AI can improve nursing work, key examples of which include reducing documentation burden and repetitive tasks, and earlier detection of patient risks and deterioration. And while studies on these and other emerging AI tools have demonstrated positive impacts on several aspects of nursing practice (Abdelmohsen & Al-Jabri, 2025), research has also reinforced the critical importance of engaging nurses in AI system design, evaluation, implementation, and ongoing monitoring (Scott et al., 2026). AI integration in healthcare delivery should augment nursing expertise and expand time for relational, compassionate care, not simply intensify productivity. Productivity outcomes of AI implementation must not be assessed only through volume of patients seen, documentation completed, or technical care provided; they must also be evaluated through patient safety, care quality, equity, and professional well-being. 

Ultimately, nurses must have the education, resources, and autonomy to recognize when AI is influencing a clinical process, assess whether its output is appropriate for a patient’s circumstances, identify possible biases, and to act upon these findings. Nursing leaders across Canada are already working through professional organizations like the Canadian Nursing Informatics Association and the Canadian Association of Schools of Nursing to develop and scale digital health education initiatives among nursing students and nurses, so that all nurses have the skills to provide appropriate AI-supported nursing care and to develop nursing-led AI solutions.

Policy initiatives to optimize the nursing workforce in health AI for all 

Policy support is also needed to address emerging issues surrounding the impact of AI on nursing work and health systems. These policies should provide nurses and all healthcare practitioners with protected time, resources, and opportunities to participate in AI design, procurement, implementation, and evaluation. There is also a rapidly escalating need for institutional and regulatory AI policies to delineate differences in professional and organizational accountability. Nurses have already called for clear guidance on how to manage situations where AI outputs conflict with their clinical judgement, when a system fails to flag deterioration, or an automated recommendation contributes to harm. Responsibility cannot fall on individual nurses when organizations and vendors control how systems are configured, staffed, and deployed, and meaningful human oversight will require authority, information, competency, and time to exercise judgement.

AI offers promising approaches to some of healthcare’s most persistent challenges, but its integration must not compromise the quality of care that every patient deserves. Nurses have always advocated for patients, and that advocacy now extends to the design, implementation, and governance of AI. The profession is positioned to champion a balanced approach in which AI augments clinical expertise, supports providers, strengthens patient outcomes, and preserves the relational and compassionate foundations of care. Nurses are uniquely placed to bridge the needs of patients, health systems, and technology developers, translating clinical and lived experience into requirements for safer AI. Governments, regulators, educators, unions, and professional associations should embed nurses across AI design, procurement, implementation, evaluation, and governance, not only as end users, but also as architects of how AI enters care and is subsequently positioned to truly benefit all.

References

Abdelmohsen, S. A., & Al-Jabri, M. M. (2025). Artificial intelligence applications in healthcare: A systematic review of their impact on nursing practice and patient outcomes. Journal of Nursing Scholarship, 57(6), 957–966. https://doi.org/10.1111/jnu.70040

Al Khatib, I., & Ndiaye, M. (2025). Examining the Role of AI in Changing the Role of Nurses in Patient Care: Systematic Review. JMIR nursing, 8, e63335. https://doi.org/10.2196/63335

Dykes, S., & Chu, C. H. (2020). Now more than ever, nurses need to be involved in technology design: lessons from the COVID‐19 pandemic. Journal of Clinical Nursing, 30(7-8), e25. https://doi.org/10.1111/jocn.15581

Innovation, Science and Economic Development Canada. (2026). Canada’s National Artificial Intelligence Strategy: AI for All. Government of Canada. 

Nature Medicine (Editorial). (2026). Show us the evidence for the value of medical AI, Nat Med, 32, 1163.  https://doi.org/10.1038/s41591-026-04389-4

Rossetti, S. C., Dykes, P. C., Knaplund, C., … Cato, K. D. (2025). Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial. Nature Medicine, 31(6), 1895–1902. https://doi.org/10.1038/s41591-025-03609-7

Scott, A. J. S., Zhao, Q., Pan, J. F., Brown, B. C., & Dowding, D. (2026). Nurses’ experiences using AI in clinical practice: Systematic review. JMIR Nursing, 9, e91238. https://doi.org/10.2196/91238