The ROBIS [22] tool was selected for its relevance in assessing the methodological rigor and potential biases inherent in systematic and scoping reviews. Specifically designed to evaluate critical aspects of review methodology, such as relevance, study eligibility criteria, selection processes, data appraisal, and synthesis integrity, ROBIS provides a structured and comprehensive framework, ensuring the highest standards of assessment. This ROBIS analysis examined 18 studies (Multimedia Appendix 3 [32-49]).

All 18 studies were deemed relevant, aligning with the umbrella review’s objectives to investigate AI’s impact on nursing practice, management, and education. The studies spanned diverse AI applications, including predictive modeling, VR, robotics, and ethical considerations, demonstrating comprehensive coverage of the subject matter.

All studies demonstrated clearly defined and rigorously applied eligibility criteria, often grounded in recognized frameworks such as PRISMA and Joanna Briggs Institute guidelines. These robust criteria enhanced the transparency and reliability of the reviews.

Systematic reviews, including the one by Gonzalez-Garcia et al [45], provided detailed eligibility justifications, strengthening the reliability of their findings. Their approach highlighted a commitment to methodological rigor and consistency.

Some reviews, such as the one by Montejo et al [36], adopted broader inclusion criteria that, while providing a comprehensive perspective, limited the depth of the insights. A more refined focus could enhance the specificity and relevance of findings.

The processes for identifying and selecting studies were generally robust, with several reviews using exhaustive multidatabase searches and citation tracking. These practices ensured the inclusion of a wide range of relevant studies and minimized the risk of publication bias.

The bibliometric analysis by Chang et al [42] and the AI in psychiatric nursing review by Li et al [48] exemplified high practices in search strategy design, capturing a broad spectrum of the literature across multiple domains.

Some reviews lacked comprehensive reporting of their search strategies, potentially limiting replicability. For example, Knop et al [44] did not fully disclose database coverage or search terms, leaving gaps in methodological transparency.

This domain exhibited variability, with several high-quality reviews using standardized data extraction and appraisal frameworks. However, inconsistencies in reporting and appraisal depth were noted in certain studies.

Gonzalez-Garcia et al [45] demonstrated meticulous attention to detail, using dual independent data extraction and rigorous appraisal methods. Similarly, Yasin et al [41] leveraged PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to enhance reliability.

In contrast, Seibert et al [43] and Rony et al [46] provided limited details on the appraisal of included studies, raising questions about the reliability of their findings. Future efforts should prioritize comprehensive reporting of quality assessments.

The synthesis of findings was a strength across many studies, with coherent thematic integration and actionable insights. However, heterogeneity in methodologies occasionally posed challenges for interpretation.

Reviews such as the ones by Buchanan et al [47], Yasin et al [41], and Chang et al [42] excelled in synthesizing findings into well-defined themes, offering clear pathways for practical application.

Some studies, such as the one by O’Connor et al [49] lacked sufficient exploration of heterogeneity, which may have limited the depth of their conclusions. Future reviews should aim to contextualize findings within the broader spectrum of methodologies and outcomes.

The overall quality is commendable. To further elevate the impact and quality of AI research in nursing, future reviews should ensure detailed and transparent documentation of methods to enhance replicability and bolster confidence in findings. Adopting universal reporting standards such as PRISMA-S (Preferred Reporting Items for Systematic reviews and Meta-Analyses Literature Search Extension) for AI-based reviews can set a new benchmark.

This umbrella review critically evaluated 18 studies on AI applications in nursing and health care using the AMSTAR 2 framework [31]. The included studies consisted of systematic reviews, scoping reviews, rapid reviews, narrative reviews, literature reviews, and meta-analyses. Each study was assessed across 8 AMSTAR 2 domains, with adaptations applied to nonsystematic review types. This evaluation provides a transparent and rigorous foundation for understanding the reliability and methodological strengths and weaknesses of the evidence base (Multimedia Appendix 4 [32-49]).

Systematic reviews such as the ones by Gonzalez-Garcia et al [45] and Ruksakulpiwat et al [38] adhered to protocol registration through platforms such as PROSPERO and Open Science Framework, demonstrating exemplary transparency. This enhances reproducibility and ensures that methods are not post hoc adjusted to fit the results. Scoping and narrative reviews, such as the one by Buchanan et al [47], followed Open Science Framework registration, while others, such as the one by Knop et al [44], did not disclose protocols.

Most systematic and scoping reviews, including the ones by O’Connor et al [49] and Yasin et al [41], conducted comprehensive and multidatabase searches. Some reviews, particularly narrative and bibliometric reviews, such as the one by Chang et al [42], lacked detailed descriptions of their search strategies, potentially missing relevant studies.

Dual-review processes were widely reported in systematic reviews, ensuring independent selection and extraction. For example, Gonzalez-Garcia et al [45] and Koo et al [37] consistently used dual reviewers with conflict resolution mechanisms. In contrast, narrative reviews and rapid reviews, such as the one by Rony et al [46], often omitted these practices, which could introduce bias into their synthesis.

Systematic reviews such as the one by Gonzalez-Garcia et al [45], used validated tools such as Joanna Briggs Institute to assess methodological quality, offering critical insights into study reliability. Scoping reviews, such as those by Buchanan et al [47], and narrative reviews generally did not evaluate the risk of bias, consistent with their exploratory nature.

Systematic reviews, such as the one by Ruksakulpiwat et al [38] and O’Connor et al [49], acknowledged heterogeneity but often lacked systematic methods to address it. Meta-analytical methods were not applied in any of the reviews included.

Narrative and scoping review findings were typically synthesized thematically or descriptively, providing valuable insights but limited by the absence of statistical heterogeneity analyses.

This AMSTAR 2 evaluation revealed a dichotomy in the quality of studies on AI applications in nursing and health care. Systematic and scoping reviews adhered to rigorous methodological standards, setting benchmarks for protocol registration, structured frameworks, and comprehensive literature searches. However, deficiencies in exploratory designs, including narrative and bibliometric reviews, underscore the need for greater transparency, formal risk of bias assessments, and systematic heterogeneity analysis.

The thematic synthesis is organized into 3 major themes: ethical and social implications, transformation of nursing education, and strategies for integration, reflecting the objectives of this review.

Table 2 summarizes the findings of the umbrella review, categorizing them into key fields, such as AI and ethics, nursing education, and clinical practice.

The integration of AI in nursing brings substantial ethical concerns, particularly regarding data privacy, algorithmic bias, and accountability. External research highlights critical failures in these domains. For instance, Obermeyer et al [50] demonstrated racial disparities in AI-driven health care predictions, revealing how biased datasets perpetuate systemic inequities. Similarly, Zou and Schiebinger [51] identified gender biases in AI algorithms, underscoring the intersection of technology with broader social justice issues. Moreover, AI’s integration in nursing enhances diagnostic accuracy yet poses substantial ethical risks, such as data privacy breaches and algorithmic bias, that demand robust, transparent frameworks [52]. These findings suggest that the ethical challenges identified in this review reflect a global issue rather than isolated shortcomings in health care systems.

Our review revealed that while AI technologies such as predictive analytics, clinical decision support systems, and VR simulations offer substantial benefits in terms of efficiency and educational enrichment, their implementation is often hampered by resistance from nursing professionals, concerns over job displacement, and the lack of standardized ethical frameworks. The black box problem, where AI decision-making lacks transparency, remains a persistent challenge in high-stakes environments such as nursing. Studies, such as the one by Floridi et al [53], have highlighted the critical need for transparent and interpretable AI systems to ensure accountability. Without transparency, errors in AI predictions or decisions may lead to adverse patient outcomes without clear avenues for redress. Regulatory frameworks such as the General Data Protection Regulation and HIPAA provide essential guidelines for safeguarding patient data privacy and security, but they fall short in addressing algorithmic opacity. The FAIR (Fairness of Artificial Intelligence Recommendations) framework emphasizes independent audits, stakeholder engagement, and algorithmic accountability to ensure fairness and inclusivity [54]. This review’s findings align with these concerns, emphasizing the urgent need for legal and operational frameworks to address accountability gaps. In addition, the pervasive issue of algorithmic bias, as identified in multiple studies, highlights the urgent need for inclusive design processes and continuous monitoring to prevent the perpetuation of health care disparities.

AI-driven tools, such as LLMs, virtual simulations, and augmented reality, are redefining nursing education by enhancing critical thinking, clinical judgment, and personalized learning experiences. However, the lack of standardized AI literacy programs and insufficient integration of AI concepts into nursing curricula limit the preparedness of future nurses. Risks of overreliance on AI tools and potential erosion of interpersonal skills also warrant careful consideration. AI-driven tools boost clinical outcomes and streamline operations, yet the absence of standardized AI literacy programs risks overreliance and erosion of essential interpersonal skills [52].

Developing structured curricula that incorporate foundational AI concepts, ethical principles, and practical simulations is essential. Educator training programs should also be prioritized to enable confident and responsible use of AI in academic and clinical settings [43,47]. Furthermore, embedding ethical considerations into nursing curricula and establishing robust regulatory frameworks are imperative to safeguard patient autonomy, data privacy, and the integrity of nursing care [44,46].

AI is reshaping the landscape of nursing practice and education, offering transformative opportunities to enhance care delivery, decision-making, and learning experiences. However, the findings of this review underscore substantial gaps in implementation and operationalization, ethical governance, equity, and curriculum integration. Addressing these challenges requires a holistic approach that prioritizes technological advancement alongside humanistic and ethical considerations.

AI’s potential to revolutionize nursing education is substantial, offering opportunities to enhance learning experiences and preparing nurses for a technologically advanced health care environment. However, the integration of AI into curricula lacks standardization.

AI-driven educational tools, such as simulation platforms and VR, have shown promise in enhancing critical thinking and clinical decision-making skills, aligning with findings from Foronda et al [55], who reported that simulation technologies enhance clinical judgment. These similarities reinforce the potential benefits of incorporating AI tools into nursing education.

Despite these opportunities, concerns about overreliance on AI and the potential erosion of interpersonal skills persist. A concern was also raised by Shorey and Ng [56], who argued that VR technological advancements should not compromise the humanistic aspects of nursing. The alignment of these findings suggests a need for balanced integration that preserves core nursing values. Furthermore, continuous professional development was identified as essential for equipping nurse educators with the skills needed to implement AI tools effectively [42,45].

Despite substantial advancements in AI research, the transition from development to operationalization remains a critical challenge. Although AI technologies—including predictive analytics, clinical decision support systems, and workflow automation—offer great potential for nursing practice, the literature shows a disproportionate emphasis on early-stage development, with only a limited number of studies addressing implementation and even fewer exploring operationalization [32]. This disconnect hinders the realization of AI’s transformative potential in real-world nursing environments, where systemic barriers such as financial constraints, infrastructure readiness, and workforce training remain inadequately addressed [39,45].

Practical strategies that leverage AI’s capabilities can offer immediate benefits to nursing practice. Automating administrative tasks such as documentation, scheduling, and resource allocation not only reduces nurse workload but also enhances workflow efficiency and patient safety [45,57]. Similarly, AI-driven predictive analytics and clinical decision support systems contribute to optimized patient outcomes by enabling early intervention and personalized care strategies [32,45]. These tools exemplify how AI can augment nursing practice, if implementation frameworks account for the unique challenges of diverse health care contexts [57].

In nursing education, AI technologies are redefining how students acquire and apply clinical skills. Tools such as LLMs, augmented reality, and VR offer immersive learning environments that enhance clinical judgment, critical thinking, and decision-making capabilities [32,36]. By simulating realistic scenarios, these technologies bridge the gap between theoretical knowledge and practical application, allowing students to refine their skills in controlled, feedback-rich settings [36,47]. Moreover, real-time AI-driven feedback systems support continuous skill improvement, ensuring that nursing graduates are well-prepared for the complexities of modern health care [47].

However, systemic challenges persist. The absence of standardized AI literacy programs and insufficient integration of AI concepts into nursing curricula limit the preparedness of future nurses to engage effectively with these technologies [39,47]. This gap risks fostering overreliance on AI systems, potentially eroding essential interpersonal skills and undermining core nursing values such as empathy and patient-centered care [45,57]. Furthermore, the lack of real-world implementation data, particularly from pilot programs and case studies, leaves critical questions unanswered regarding how AI can be sustainably and equitably operationalized in resource-limited settings [32].

Addressing these gaps requires a strategic shift in focus. Future research should prioritize the development of adaptable frameworks that guide the integration of AI into nursing practice and education. These frameworks must consider not only the technical capabilities of AI but also the systemic and infrastructural changes required to support equitable and sustainable implementation. Effective AI integration in nursing hinges on interdisciplinary collaboration and longitudinal research, especially in resource-limited settings, to ensure sustainable and equitable implementation [52].

AI adoption is marred by disparities in access, particularly in resource-limited settings. Inclusive policy frameworks, as advocated by Wachter and Cassel [58], are essential to bridge these gaps. Investments in localized AI deployment strategies, such as culturally sensitive designs and infrastructure development in underserved regions, can ensure that the benefits of AI are equitably distributed. Moreover, the pervasive issue of algorithmic bias, as identified in multiple studies, highlights the urgent need for inclusive design processes and continuous monitoring to prevent the perpetuation of health care disparities [34,41]. Ensuring equitable access to AI tools remains a pressing concern, and regulatory frameworks must address these disparities to promote fairness and inclusivity in AI applications [42,45].

From a sociotechnical perspective [59], successful AI integration in nursing requires alignment between technological tools, the organizational context in which they are deployed, and the nurses who use them. This echoes the Technology Acceptance Model [60], which posits that perceived usefulness and ease of use critically influence adoption. By integrating sociotechnical considerations, such as workflow compatibility, nurse involvement in AI design, and ethical guardrails, future implementations are more likely to foster positive attitudes, reduce resistance, and ultimately support the ethical, patient-centered use of AI in nursing practice.

Developing robust regulatory guidelines is paramount to ensuring responsible AI adoption in nursing practice. Such guidelines should extend beyond established frameworks such as General Data Protection Regulation and HIPAA, considering the unique challenges posed by AI-driven clinical decisions, algorithmic biases, and data privacy. Regular audits for fairness and security are equally essential. By mandating periodic checks for algorithmic integrity and data protection, health care institutions can detect and address biases early, thus safeguarding both patient welfare and public trust.

Preparing future nurses for an increasingly technology-driven environment requires a systematic revision of nursing curricula to incorporate foundational AI concepts, data literacy, and practical ethical training. This approach empowers nurses to critically evaluate AI-generated outputs and to collaborate effectively with advanced digital tools. Equally important is faculty development: providing educators with the knowledge and confidence to integrate AI-based teaching methods not only enhances learning outcomes but also fosters a workforce adept at navigating complex digital landscapes.

Promoting partnerships among nurses, data scientists, ethicists, and engineers encourages the design and implementation of AI systems that are ethical, user-centered, and clinically relevant. By including patient and community representatives in these discussions, developers gain insights into linguistic, cultural, and socioeconomic nuances that might otherwise be overlooked. This collaborative dynamic cultivates AI solutions that are not only technologically sound but also equitable and broadly acceptable.

Enhancing the interpretability of AI models is vital for building trust among nurses, patients, and decision-makers. Algorithms capable of explaining their decision pathways help ensure that clinical judgments remain transparent and can be scrutinized for biases or errors. Beyond transparency, defining clear protocols for handling AI-related adverse events establishes accountability and reinforces patient safety. When responsibilities are delineated at organizational and professional levels, it becomes clearer how to address unintended consequences and system failures.

Substantial investments are needed to extend AI technologies to underserved regions, ensuring that socioeconomic constraints do not widen the existing digital divide. By deploying AI tools that are culturally tailored, accommodating linguistic and demographic variations, health care delivery can become more inclusive. In this way, AI can serve as a powerful lever for enhancing rather than exacerbating health equity, particularly when implemented with deliberate planning and resource allocation.

Ongoing education and training initiatives enable practicing nurses to stay abreast of cutting-edge AI tools, emerging data management techniques, and shifting ethical standards. Designing accessible modules, whether web-based or blended learning, can promote widespread engagement. Moreover, creating incentives and funding for nurse-led AI research helps spawn innovative pilot programs that link theoretical advancements to concrete clinical outcomes, fostering a cycle of continual improvement.

Nurses have long been trusted figures in health care and positioning them as leaders on AI-related boards, committees, and decision-making panels ensures that ethical considerations remain central to technological progress. Strengthening ethics education that prioritizes patient autonomy, consent, and relational care enables nurse leaders to critically assess both the potential and the pitfalls of AI. By uniting professional values with forward-looking strategies, nursing professionals can guide AI initiatives that enhance patient care while preserving the core compassion of the discipline.