Table 1. Actionable suggestions based on the identified priority areas.

Priority	Recommendations
	Education	Practice	Research	Leadership
1.Nurses must understand the relationship between data they collect and AI technology	Nursing educators should consider the creation of a regional AI4N Taskforce to develop the ‘Minimum AI in Nursing Competencies’ curriculum for nursing undergraduate education (linked with Priority 3). Nursing educational programmes and continuing education should prioritize recruiting faculty with expertise in health informatics and technology development. Nursing educational programmes that are unable to recruit faculty with health informatics or technology development expertise have a responsibility to identify alternative ways of ensuring content related to the ‘minimum AI in nursing competencies’ are delivered. This can be achieved by partnering with professional health informatics groups and/or experts at other institutions and/or partnering with computer or information science departments. Nursing educators need to ensure that nursing curricula at all levels should have appropriate integration of AI knowledge to ensure nurses are equipped to practice with the knowledge, skills and judgement required to work in health systems that use Al.	Nursing leaders should create an organizational AI4N Taskforce to develop the ‘Minimum AI in Nursing Competencies’ curriculum for practising nurses (linked with Priority 3) that can be delivered as part of new employee orientations and as continuing education. Nurses need options for in-practice training on the specific AI technologies they use. Nursing stakeholders need to create structures that promote a continuous discussion of the implications of AI technologies in nursing on all levels. Nursing organizations need to develop guidelines for the implementation of AI technologies to ensure safe use of AI. AI-system developers need to make AI-system outputs transparent for nurses.	Nursing researchers should focus on the use and impact of AI in nursing and the impacts related to workforce, clinical and patient health outcomes as well as making the AI lifecycle explainable, from AI conception to implementation. Nursing researchers should focus on the contributions of nursing to AI technology development and implementation.	Nursing leaders need to have an understanding of AI technologies to be able to lead the implementation of these technologies and support clinical teams on its use. Nursing leaders need to create opportunities for further education and training on AI4N for staff (educators and clinicians). Nursing leaders need to promote nurses’ attitudes towards learning about the AI technologies they use.
2. Nurses must be involved in all stages of AI creation: from development to implementation	Educational institutions should facilitate the development of partnerships and collaborations between nursing educators and technology teams, to provide nursing students in all levels an opportunity to work in an interdisciplinary setting and get involved in technology development. Existing examples of such programmes can be used to inform the development of bespoke programmes (e.g. see the University of Turku’s Master’s joint degree programme in future health and technology that accepts both nursing students and technology students; University of Turku, 2020). Nursing educators should acknowledge current theories on technology development to support the rigour and the respect to all stages of technology development for secure and safe AI products. Nursing educators should develop advanced educational training for nurses who are interested in taking on more active and hands-on roles in the development and implementation of AI technologies in health systems.	Nurses should play an active role in AI technology development and deployment in clinical settings to ensure that technology is integrated into the clinical workflows, patient and caregiver perspectives are addressed and potential unintended consequences are forecasted.	Research entities and funding mechanisms should encourage participatory and co-produced research designs in health AI research to leverage nursing expertise in relational practice. Research entities and funding mechanisms needed to support the development of AI or related technologies that target nursing practice and establish programmes of research in this underdeveloped field.	Leaders should build organizational structures to afford nurses opportunities to be involved in all stages of AI creation.
3. “AI for good nursing”: AI must be used to help nurses be better at what they do	Nursing education programmes can use virtual environments and/or simulations mirroring real case studies to study AI implications. These would focus on the provision of patient-centred and relational care while using AI technologies; assessment of patients’ digital literacy and digital privacy and security as part of the informed consent process; understanding the impacts of AI technology use on practice. Nursing educators need to leverage data already collected (e.g. simulation labs) to further develop nursing education and support critical thinking.	All stakeholders need to ensure that AI technology should be used to help nurses allocate more time for providing preventative health recommendations to patients and patient populations. All stakeholders need to ensure that AI technologies (e.g. clinical decisions support systems) incorporate a holistic patient perspective, support care provision based on patient’s goals and priorities, and proactively consider ethical concerns that can arise from using the technology, as part of the development process. All stakeholders need to ensure that AI technology supports fundamental care processes in a way that supports critical thinking and meaningful care decisions. Practising nurses need to ensure they are knowledgeable about potential areas of bias related to data collection and subsequent use in AI technologies (e.g. identification of decontextualized data, identification of potential areas where existing inequities may be exacerbated by AI tools). Al-developers need to ensure that clear guidance, protocols, and systems need to be developed and established in healthcare organizations to enable nurses to flag AI technologies being used that are potentially questionable, result in patient harm, or exacerbate existing inequities.	Nursing researchers need to study what types of AI technologies are needed to augment nursing critical-thinking and care skills. Nursing researchers need to examine how AI is going to impact nursing workflow and care outcomes. Nursing researchers need to explore how equity and social justice considerations can be incorporated in the design and development of AI technologies.	Health systems leaders and nursing leadership need to ensure that achieving economic efficiencies is not the sole driver of AI implementation; AI technologies can be used to help nurses with specific skill-based tasks to afford more time for higher-order cognitive tasks and critical thinking. There are existing efforts that can be built on to better evaluate the impacts of AI technologies on quality of care. For example, the work towards developing metrics of nursing value from electronic health records (Pruinelli et al., 2016; Welton & Harper, 2016). Nurse leaders should be key advocates to ensuring that AI use takes a more proactive, rather than a reactive approach that is currently seen in healthcare. This includes ensuring that key variables for nursing care and outcomes, and variables related to social determinants of health and equity are considered in predictive modelling and development of clinical decision support systems. Leaders should also be key proponents for data integration and the combination of multiple data sources to provide more valuable insights than those available in single sources. Leaders should also be proactive in identifying opportunities for massive data where the biggest potential lurks, based on understandings of nursing practice and subsequent impacts on populations.