By Raga Bjarnadottir, PhD, MPH
International Nurses Day passed on May 12, anchored this year in an International Council of Nurses theme that lands with unusual weight: Our Nurses. Our Future. Empowered Nurses Save Lives. The International Council of Nurses (ICN) names the moment plainly. The world faces “converging crises that make the impact of nursing more important than ever,” including geopolitical conflicts, climate-related disasters, workforce shortages, deepening inequalities and growing chronic and mental health needs (ICN, 2026).
To that list, we would add another critical shift: the rapid growth of AI in health care and the widening gap between technical promise and clinical reality. Recognizing the power of nursing is, increasingly, also a question of whether nursing science shapes the systems that will define the next decade of care.
A Decade of Growth
In 2016, an international survey of 373 nursing informatics researchers and practitioners across 44 countries named big data science, standardized terminologies, clinical decision support and patient safety as the field’s top priorities; AI was not yet at the center of the conversation (Peltonen et al., 2016). In 2019, the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative convened a global think tank to position nursing as a driver, not an observer, of AI in health systems, with explicit priorities around nursing’s role across the AI development lifecycle (Ronquillo et al., 2021). By 2025, a comprehensive review found AI woven through nursing informatics across diagnostics, monitoring, documentation and care coordination, with explicit calls for ethical frameworks and AI literacy programs to match the pace of adoption (Nashwan et al., 2025). The arc, from terminologies and standards to global think tanks to literacy as a core competency, is the story of a discipline maturing in real time.
Why this Matters for Precision Health
Precision health promises care tailored to the person, not the average patient. But personalization is only as good as the data it draws on, and the data that distinguishes individual patient trajectories, including mobility, skin integrity, cognition, pain, social context and the patient’s own voice, is largely captured by nurses. Vital signs, labs and imaging cannot fully capture these dimensions. Nursing assessments do. That makes nursing data the backbone, not a supplement, of precision health. Yet, in reviewing the current evidence base on multimodal clinical AI, we find that nursing-relevant data are still largely absent as distinct inputs in deployed systems. The capability exists. The design choices have not caught up.
Empowerment, Applied to Data
ICN’s call for empowered nursing is structural: investment, working conditions, scope of practice and leadership at every level. It is also a data infrastructure agenda. Empowered nurses save lives in part by making the patient’s full clinical picture visible to the systems that increasingly shape care decisions. That means nursing-led design at the point of data definition, AI literacy built into nursing curricula and insistence that nursing-sensitive outcomes such as falls, pressure injuries, functional decline and symptom burden sit alongside the diagnostic and mortality endpoints that currently dominate AI evaluation.
Nurses are the largest segment of the clinical workforce and the primary point of integration for everything a patient experiences. The growth of nursing science and AI over the past decade has been real. The next step is ensuring that precision health is built on a nursing data foundation.
References:
International Council of Nurses. (2026). International Nurses Day 2026: Our Nurses. Our Future. Empowered Nurses Save Lives. Geneva: ICN.
Nashwan, A. J., Cabrega, J. C. A., Othman, M. I., Khedr, M. A., Osman, Y. M., El-Ashry, A. M., Naif, R., & Mousa, A. A. (2025). The evolving role of nursing informatics in the era of artificial intelligence. International Nursing Review, 72(1), e13084. https://doi.org/10.1111/inr.13084
Peltonen, L.-M., Topaz, M., Ronquillo, C., Pruinelli, L., Sarmiento, R. F., Badger, M. K., Ali, S., Lewis, A., Georgsson, M., Jeon, E., Tayaben, J. L., Kuo, C.-H., Islam, T., Sommer, J., Jung, H., Eler, G. J., & Alhuwail, D. (2016). Nursing Informatics Research Priorities for the Future: Recommendations from an International Survey. Studies in Health Technology and Informatics, 225, 222–226. https://doi.org/10.3233/978-1-61499-658-3-222
Ronquillo, C. E., Peltonen, L.-M., Pruinelli, L., Chu, C. H., Bakken, S., Beduschi, A., Cato, K., Hardiker, N., Junger, A., Michalowski, M., Nyrup, R., Rahimi, S., Reed, D. N., Salakoski, T., Salanterä, S., Walton, N., Weber, P., Wiegand, T., & Topaz, M. (2021). Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative. Journal of Advanced Nursing, 77(9), 3707–3717. https://doi.org/10.1111/jan.14855