Bridging Global Nursing Divides: Advancing Equity and Workforce Sustainability Through Inclusive AI Integration in Education, Research, and Practice
1
School of Nursing & Shannon School of Business, Cape Breton University, Sydney, Canada
2
School of Nursing, Cape Breton University, Sydney, NS Canada Affiliate Researcher - Unit of Occupational Medicine, Institute of Environmental Medicine, Karolinska Institute, Sweden, Sydney, Canada, Sydney, Canada
Popul. Med. 2026;8(Supplement Supplement 1):
ABSTRACT
BACKGROUND:
The global nursing shortage persists, with an estimated 13 million additional nurses needed by 2030 across the globe, yet AI solutions with the potential to support nurses’ work are not well understood and remain inequitably distributed, threatening to widen existing healthcare disparities. While healthcare AI investments reached $26.57 billion globally in 2024, nursing-specific applications remain underrepresented, and critical equity gaps persist.
METHODS:
We conducted a critical narrative review of global healthcare AI literature (July 2024-February 2025) with a focus on applications across nursing education, research, and clinical practice. Data analysis prioritized equity considerations, accessibility barriers, and sustainability factors affecting AI adoption in diverse geographic, economic, and cultural nursing contexts.
RESULTS:
AI demonstrates transformative potential to address global nursing workforce challenges while also advancing health equity if implemented inclusively. Educational applications include culturally responsive adaptive learning technologies enabling personalized, multilingual instruction and competency-based assessments. Research benefits encompass democratized data analysis tools and predictive modeling capabilities that can strengthen evidence generation in under-researched nursing topics and contexts. Clinical applications offer real-time decision support, streamlined documentation reducing administrative burden, and enhanced diagnostic accuracy that can extend specialist expertise to underserved areas. Evidence demonstrates that sustainable, equitable AI integration requires: (1) participatory co-design engaging nurses across all geographic and economic contexts, (2) culturally adapted, multilingual AI literacy programs accessible to the global workforce, (3) robust ethical frameworks addressing bias, privacy, and professional autonomy across diverse regulatory environments, and (4) infrastructure investments ensuring technology access transcends resource availability.
CONCLUSIONS:
Transforming nursing through AI requires dismantling systemic barriers to ensure technological benefits reach all nurses and patients globally, regardless of geography or economic status. Evidence-informed strategies must prioritize inclusive workforce development, equitable access to AI tools and training, and participatory governance frameworks that center diverse nursing voices in technology design and implementation.
The global nursing shortage persists, with an estimated 13 million additional nurses needed by 2030 across the globe, yet AI solutions with the potential to support nurses’ work are not well understood and remain inequitably distributed, threatening to widen existing healthcare disparities. While healthcare AI investments reached $26.57 billion globally in 2024, nursing-specific applications remain underrepresented, and critical equity gaps persist.
METHODS:
We conducted a critical narrative review of global healthcare AI literature (July 2024-February 2025) with a focus on applications across nursing education, research, and clinical practice. Data analysis prioritized equity considerations, accessibility barriers, and sustainability factors affecting AI adoption in diverse geographic, economic, and cultural nursing contexts.
RESULTS:
AI demonstrates transformative potential to address global nursing workforce challenges while also advancing health equity if implemented inclusively. Educational applications include culturally responsive adaptive learning technologies enabling personalized, multilingual instruction and competency-based assessments. Research benefits encompass democratized data analysis tools and predictive modeling capabilities that can strengthen evidence generation in under-researched nursing topics and contexts. Clinical applications offer real-time decision support, streamlined documentation reducing administrative burden, and enhanced diagnostic accuracy that can extend specialist expertise to underserved areas. Evidence demonstrates that sustainable, equitable AI integration requires: (1) participatory co-design engaging nurses across all geographic and economic contexts, (2) culturally adapted, multilingual AI literacy programs accessible to the global workforce, (3) robust ethical frameworks addressing bias, privacy, and professional autonomy across diverse regulatory environments, and (4) infrastructure investments ensuring technology access transcends resource availability.
CONCLUSIONS:
Transforming nursing through AI requires dismantling systemic barriers to ensure technological benefits reach all nurses and patients globally, regardless of geography or economic status. Evidence-informed strategies must prioritize inclusive workforce development, equitable access to AI tools and training, and participatory governance frameworks that center diverse nursing voices in technology design and implementation.
| eISSN: | 2654-1459 |
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