Integrating Artificial Intelligence (AI) in nursing practice is revolutionising healthcare by enhancing clinical decision-making and patient care. However, the adoption of AI by registered nurses, especially in varied healthcare settings such as Saudi Arabia, remains underexplored. Understanding the facilitators and barriers from the perspective of frontline nurses is crucial for successful AI implementation. This study aimed to explore registered nurses' perspectives on the facilitators and barriers to AI adoption in nursing practice in Saudi Arabia and to propose an extended Technology Acceptance Model for AI in Nursing (TAM-AIN). A qualitative study utilising focus group discussions was conducted with 48 registered nurses from four major healthcare facilities in Al-Kharj, Saudi Arabia. Thematic analysis, guided by the Technology Acceptance Model framework, was employed to analyse the data. Key facilitators of AI adoption included perceived benefits to patient care (85%), strong organisational support (70%), and comprehensive training programs (75%). Primary barriers involved technical challenges (60%), ethical concerns regarding patient privacy (55%), and fears of job displacement (45%). These findings led to the development of TAM-AIN, an extended model that incorporates additional constructs such as ethical alignment, organisational readiness, and perceived threats to professional autonomy. AI adoption in nursing practice requires a holistic approach that addresses technical, educational, ethical, and organisational challenges. The proposed TAM-AIN offers a comprehensive framework for optimising AI integration into nursing practice, emphasising the importance of nurse-centred implementation strategies. This model provides healthcare institutions and policymakers with a robust tool to facilitate successful AI adoption and enhance patient outcomes.

Introduction: Worldwide, healthcare systems aim to achieve the best possible quality of care at an affordable cost while ensuring broad access for all populations. The use of artificial intelligence (AI) in healthcare holds promise to address these challenges through the integration of real-world data-driven insights into patient care processes. This study aims to assess nurses’ awareness and attitudes toward AI-integrated tools used in clinical practice.Methods: A descriptive cross-sectional design captured nurses’ responses at three governmental hospitals in Saudi Arabia by using an online questionnaire administered over 4 months. The study involved 220 registered nurses with a minimum of one year of clinical experience, selected through a convenience sampling method. The online survey consisted of three sections: demographic information, an assessment of nurses’ AI knowledge, and the general attitudes toward the AI scale.Results: Nurses displayed “moderate” levels of awareness toward AI technology, with 70.9% having basic information about AI and only 58.2% (128 nurses) were considered “aware” of AI as they dealt with one of its healthcare applications. Nurses expressed openness to AI integration ( M = 3.51) on one side, but also had some concerns about AI. Nurses expressed conservative attitudes toward AI, with significant differences observed based on gender (χ² = 4.67, p < 0.05). Female nurses exhibited a higher proportion of negative attitudes compared to male nurses. Significant differences were also found based on age (χ² = 9.31, p < 0.05), with younger nurses demonstrating more positive attitudes toward AI compared to their older counterparts. Educational background yields significant differences (χ² = 6.70, p < 0.05), with nurses holding undergraduate degrees exhibiting the highest positive attitudes. However, years of nursing experience did not reveal significant variations in attitudes.Conclusion: Healthcare and nursing administrators need to work on increasing the nurses’ awareness of AI applications and emphasize the importance of integrating such technology into the systems in use. Moreover, addressing nurses’ concerns about AI's control and discomfort is crucial, especially considering generational differences, with younger nurses often having more positive attitudes toward technology. Change management strategies may help overcome any hindrances.

Background: Global population aging poses challenges for healthcare. Digital health technologies may benefit older adults through enhanced access, monitoring, and self-care. This systematic review evaluates the intersection of digital health interventions and healthy aging, focusing on adoption, efficacy, and user experience. Methods: PubMed, Embase, and Cochrane Library were systematically searched for studies on digital health technologies for adults aged 50+ years. Randomized controlled trials, observational studies, surveys, and qualitative studies were included. Outcomes were adoption rates, efficacy, and qualitative feedback. Study quality was assessed using standardized tools. Results: 15 studies were included. Adoption increased during COVID-19, but divisions persist. Barriers like technology challenges and distrust require addressing. Web-based programs and telerehabilitation demonstrated benefits for behaviors and balance. Users had positive attitudes but emphasized patient-centric, ethical design. Most efficacy data were preliminary; more rigorous trials are needed. Discussion: Digital health interventions show promise for supporting healthy aging, but thoughtful implementation strategies tailored to user needs and capacities are essential to realizing benefits equitably. More efficacy research and studies on real-world integration and ethics are warranted. Conclusions: Digital health has significant potential for promoting healthy aging through enhanced access, monitoring, and self-care. However, evidence-based, patient-centered solutions are imperative to maximize adoption, efficacy, and positive user experience for diverse older adult populations.

Objectives: This systematic review aimed to synthesize evidence on the ethical dilemma’s nurses encounter in end-of-life care and effective palliative care practices. The objectives were to understand key ethical issues, evaluate communication and decision-making strategies, and identify approaches to support nurses and patients. Methods: A comprehensive search of major databases was conducted according to the PRISMA guidelines. Studies directly relating to nursing ethics, challenges in end-of-life decision-making, and palliative care practices were included. The risk of bias was assessed using ROBVIS-II. Data on ethical issues, palliative interventions, and outcomes was extracted and analyzed thematically. Results: 22 studies met the inclusion criteria. Key themes that emerged were: (1) Effective communication and involving patients in decision-making are essential but complex. (2) Nurses face dilemmas around balancing autonomy, beneficence and relational issues. (3) Integrating palliative care principles enhances symptom management and aligns care with patient values. (4) Education and organizational support are needed to equip nurses with skills and coping strategies. Conclusion: Navigating end-of-life care requires addressing interconnected ethical, communication and support needs. While studies provided insights, further research is required on cultural competence training, standardized education programs and longitudinal evaluations.

Neonatal nurses in high-risk Neonatal Intensive Care Units (NICUs) navigate complex, time-sensitive clinical decisions where accuracy and judgment are critical. Generative artificial intelligence (AI) has emerged as a supportive tool, yet its integration raises concerns about its impact on nurses’ decision-making, professional autonomy, and organizational workflows. This study explored how neonatal nurses experience and integrate generative AI in clinical decision-making, examining its influence on nursing practice, organizational dynamics, and cultural adaptation in Saudi Arabian NICUs. An interpretive phenomenological approach, guided by Complexity Science, Normalization Process Theory, and Tanner’s Clinical Judgment Model, was employed. A purposive sample of 33 neonatal nurses participated in semi-structured interviews and focus groups. Thematic analysis was used to code and interpret data, supported by an inter-rater reliability of 0.88. Simple frequency counts were included to illustrate the prevalence of themes but were not used as quantitative measures. Trustworthiness was ensured through reflexive journaling, peer debriefing, and member checking. Five themes emerged: (1) Clinical Decision-Making, where 93.9% of nurses reported that AI-enhanced judgment but required human validation; (2) Professional Practice Transformation, with 84.8% noting evolving role boundaries and workflow changes; (3) Organizational Factors, as 97.0% emphasized the necessity of infrastructure, training, and policy integration; (4) Cultural Influences, with 87.9% highlighting AI’s alignment with family-centered care; and (5) Implementation Challenges, where 90.9% identified technical barriers and adaptation strategies. Generative AI can support neonatal nurses in clinical decision-making, but its effectiveness depends on structured training, reliable infrastructure, and culturally sensitive implementation. These findings provide evidence-based insights for policymakers and healthcare leaders to ensure AI integration enhances nursing expertise while maintaining safe, patient-centered care.

Background: Nursing incivility, defined as disrespectful behaviour toward nurses, is increasingly recognized as a pressing issue that affects nurses’ well-being and quality of care. However, research on the pathways linking incivility to outcomes is limited, especially in Saudi hospitals. Methods: This cross-sectional study examined relationships between perceived nursing incivility, nurse stress, patient engagement, and health outcomes in four Saudi hospitals. Using validated scales, 289 nurses and 512 patients completed surveys on exposure to incivility, stress levels, activation, and medication adherence. The outcomes included readmissions at 30 days and satisfaction. Results: More than two-thirds of nurses reported experiencing moderate to severe workplace incivility. Correlation and regression analyzes revealed that nursing incivility was positively associated with nursing stress. An inverse relationship was found between stress and patient participation. Serial mediation analysis illuminated a detrimental cascade, incivility contributing to increased nurse stress, subsequently diminishing patient engagement, ultimately worsening care quality. Conclusions The findings present robust evidence that nursing incivility has adverse ripple effects, directly impacting nurse well-being while indirectly affecting patient outcomes through reduced care involvement. Practical implications advocate for systemic interventions focused on constructive nursing cultures and patient empowerment to improve both healthcare provider conditions and quality of care. This study provides compelling information to inform policies and strategies to mitigate workplace mistreatment and encourage participation among nurses and patients to improve health outcomes.