Background: Artificial intelligence (AI) is rapidly transforming health care, offering substantial advancements in patient care, clinical workflows, and nursing education. Objective: This umbrella review aims to evaluate the integration of AI into nursing practice and education, with a focus on ethical and social implications, and to propose evidence-based recommendations to support the responsible and effective adoption of AI technologies in nursing. Methods: We included systematic reviews, scoping reviews, rapid reviews, narrative reviews, literature reviews, and meta-analyses focusing on AI integration in nursing, published up to October 2024. A new search was conducted in January 2025 to identify any potentially eligible reviews published thereafter. However, no new reviews were found. Eligibility was guided by the Sample, Phenomenon of Interest, Design, Evaluation, Research type framework; databases (PubMed or MEDLINE, CINAHL, Web of Science, Embase, and IEEE Xplore) were searched using comprehensive keywords. Two reviewers independently screened records and extracted data. Risk of bias was assessed with Risk of Bias in Systematic Reviews (ROBIS) and A Measurement Tool to Assess Systematic Reviews, version 2 (AMSTAR 2), which we adapted for systematic and nonsystematic review types. A thematic synthesis approach, conducted independently by 2 reviewers, identified recurring patterns across the included reviews. Results: The search strategy yielded 18 eligible studies after screening 274 records. These studies encompassed diverse methodologies and focused on nursing professionals, students, educators, and researchers. First, ethical and social implications were consistently highlighted, with studies emphasizing concerns about data privacy, algorithmic bias, transparency, accountability, and the necessity for equitable access to AI technologies. Second, the transformation of nursing education emerged as a critical area, with an urgent need to update curricula by integrating AI-driven educational tools and fostering both technical competencies and ethical decision-making skills among nursing students and professionals. Third, strategies for integration were identified as essential for effective implementation, calling for scalable models, robust ethical frameworks, and interdisciplinary collaboration, while also addressing key barriers such as resistance to AI adoption, lack of standardized AI education, and disparities in technology access. Conclusions: AI holds substantial promises for revolutionizing nursing practice and education. However, realizing this potential necessitates a strategic approach that addresses ethical concerns, integrates AI literacy into nursing curricula, and ensures equitable access to AI technologies. Limitations of this review include the heterogeneity of included studies and potential publication bias. Our findings underscore the need for comprehensive ethical frameworks and regulatory guidelines tailored to nursing applications, updated nursing curricula to include AI literacy and ethical training, and investments in infrastructure to promote equitable AI access. Future research should focus on developing standardized implementation strategies and evaluating the long-term impacts of AI integration on nursing practice and patient outcomes.

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes.

Vision sciences has traditionally been a quantitative discipline. However, to fully capture all aspects of clinical vision care, researchers increasingly need to be conversant in both quantitative and qualitative methodologies. This has resulted in qualitative methodologies becoming more common in vision sciences research literature. From the authors’ perspective, vision researchers often struggle to identify suitable qualitative methodologies when coming from a tradition of a realist ontology, or the view that independent truth exists. This study explores the ontological and epistemological considerations when approaching qualitative research in vision sciences and proposes framework analysis as a qualitative methodology that is accessible for vision scientists. Framework analysis is a flexible and highly utilitarian qualitative analysis method which complements quantitative methodologies. This paper also presents a step-by-step guide for conducting framework analysis in a logical, transparent, and repeatable way that will provide a clear audit trail of how results are obtained from subjective data. This is done using a worked example from a recent eye care study.

Aim: To assist policy-makers in improving access to eye care in under-served areas by analysing the relationship between motivational factors affecting the uptake of task-shifting in eye care and the recruitment and retention of optometrists in remote and rural areas. Background: The World Health Organization recommends two key strategies in tackling preventable blindness in under-served areas: improving human resources for health and task-shifting. The relationship between task-shifting and recruitment and retention of eye care workers in under-served areas is unknown. Ghana and Scotland are two countries from different levels of economic development that have notably expanded the roles of optometrists and struggle with rural recruitment and retention. Methods: Motivation was explored through semi-structured interviews with 19 optometrists in Ghana and Scotland with experience in remote and rural practice. Framework analysis was used to analyse interviews, explore the relationship between task-shifting and recruitment and retention and create recommendations for policy. Findings: The main motivational considerations included altruism, quality of life, learning and career opportunities, fulfilling potential, remuneration, stress of decision-making and collaboration. Motivational and demotivational factors for task-shifting and recruitment/retention shared many similar aspects. Discussion: Recruitment and retention in remote and rural areas require staff be incentivised to take up those positions, motivated to remain and given the adequate resources for personal and professional fulfilment. Task-shifting also requires incentivisation, motivation to continue and the resources to be productive. Many motivational factors influencing recruitment/retention and task-shifting are similar suggesting these two strategies can be compatible and complementary in improving access to eye care, although some factors are culture and context specific. Understanding optometrists’ motivation can help policy-makers improve rural recruitment and retention and plan services.

BACKGROUND Artificial intelligence (AI) is rapidly transforming health care, offering substantial advancements in patient care, clinical workflows, and nursing education. OBJECTIVE This umbrella review aims to evaluate the integration of AI into nursing practice and education, with a focus on ethical and social implications, and to propose evidence-based recommendations to support the responsible and effective adoption of AI technologies in nursing. METHODS We included systematic reviews, scoping reviews, rapid reviews, narrative reviews, literature reviews, and meta-analyses focusing on AI integration in nursing, published up to October 2024. A new search was conducted in January 2025 to identify any potentially eligible reviews published thereafter. However, no new reviews were found. Eligibility was guided by the Sample, Phenomenon of Interest, Design, Evaluation, Research type framework; databases (PubMed or MEDLINE, CINAHL, Web of Science, Embase, and IEEE Xplore) were searched using comprehensive keywords. Two reviewers independently screened records and extracted data. Risk of bias was assessed with Risk of Bias in Systematic Reviews (ROBIS) and A Measurement Tool to Assess Systematic Reviews, version 2 (AMSTAR 2), which we adapted for systematic and nonsystematic review types. A thematic synthesis approach, conducted independently by 2 reviewers, identified recurring patterns across the included reviews. RESULTS The search strategy yielded 18 eligible studies after screening 274 records. These studies encompassed diverse methodologies and focused on nursing professionals, students, educators, and researchers. First, ethical and social implications were consistently highlighted, with studies emphasizing concerns about data privacy, algorithmic bias, transparency, accountability, and the necessity for equitable access to AI technologies. Second, the transformation of nursing education emerged as a critical area, with an urgent need to update curricula by integrating AI-driven educational tools and fostering both technical competencies and ethical decision-making skills among nursing students and professionals. Third, strategies for integration were identified as essential for effective implementation, calling for scalable models, robust ethical frameworks, and interdisciplinary collaboration, while also addressing key barriers such as resistance to AI adoption, lack of standardized AI education, and disparities in technology access. CONCLUSIONS AI holds substantial promises for revolutionizing nursing practice and education. However, realizing this potential necessitates a strategic approach that addresses ethical concerns, integrates AI literacy into nursing curricula, and ensures equitable access to AI technologies. Limitations of this review include the heterogeneity of included studies and potential publication bias. Our findings underscore the need for comprehensive ethical frameworks and regulatory guidelines tailored to nursing applications, updated nursing curricula to include AI literacy and ethical training, and investments in infrastructure to promote equitable AI access. Future research should focus on developing standardized implementation strategies and evaluating the long-term impacts of AI integration on nursing practice and patient outcomes.

Background/Objectives: Artificial intelligence (AI) is increasingly being evaluated for ophthalmic diagnosis, screening, and triage, yet its role in paediatric eye care remains less established than in adult ophthalmology. This systematic review aimed to synthesise evidence on AI-enabled tools for paediatric ophthalmic diagnosis, screening, triage, surveillance, and referral, with an emphasis on diagnostic performance, safety, workflow integration, equity, and implementation readiness in primary, community, and primary care-relevant settings. Methods: A PRISMA-guided systematic review was conducted using MEDLINE, Embase, Web of Science, Scopus, and IEEE Xplore from inception to 30 March 2026. Eligible studies evaluated AI or machine-learning tools for children and adolescents aged 0–18 years in relation to paediatric eye conditions. Study selection and data extraction were undertaken independently by reviewers, with disagreements resolved by consensus or third-reviewer adjudication. Methodological and reporting quality was evaluated using an author-adapted six-domain rubric informed by APPRAISE-AI. Diagnostic-accuracy studies were assessed using an author-adapted QUADAS-2 framework incorporating QUADAS-AI-informed AI-specific considerations, the prediction-model study was assessed using PROBAST+AI, and the non-randomised treatment-effect study was assessed using ROBINS-I. The public dataset descriptor was evaluated separately using an author-developed dataset-quality, representativeness, and applicability framework. Because of clinical and methodological heterogeneity, findings were synthesised thematically. Results: Twelve empirical studies and one public dataset descriptor were included, covering retinopathy of prematurity, retinoblastoma, amblyopia risk, myopia, congenital cataract, and visual-acuity assessment. AI systems frequently demonstrated promising diagnostic or screening performance, including sensitivity-first detection of treatment-requiring retinopathy of prematurity, high discrimination for retinoblastoma activity, and strong myopia prediction using fundus images. Several studies supported feasibility in neonatal, school, and community workflows using smartphone-based imaging, task-shifted operators, tele-referral, and human-in-the-loop review. However, external and temporal validation, calibration, patient-level reporting, subgroup and fairness assessment, and economic evaluation were limited. Conclusions: AI-enabled tools show promise for supporting selected paediatric ophthalmic screening, triage, and surveillance pathways, particularly when combined with image-quality control, explicit escalation, and human oversight. However, confidence in the reported performance is limited by single-centre studies and enriched samples, small numbers of clinically important cases, heterogeneous analytical units, potentially optimistic aggregation procedures, limited external or temporal validation, incomplete calibration, and absent fairness analyses. Routine autonomous implementation remains premature.