Background As AI becomes increasingly embedded in healthcare systems, nursing governance faces new challenges involving ethical accountability, professional autonomy, data stewardship, and institutional oversight. Existing reviews highlight fragmented understanding of how these changes impact the nursing profession. Aim This umbrella review aimed to synthesize ethical and policy dimensions related to the integration of artificial intelligence (AI) within nursing governance and regulatory frameworks. Methods Following JBI guidance and PRISMA 2020, five databases were searched for reviews published from January 2010 to December 2025. Reviews were appraised and synthesized by purpose, quality, nursing specificity, and primary study overlap, which was quantified using a citation matrix and Corrected Covered Area (CCA). Findings Thirty-one reviews included 23 evidence syntheses and eight evidence maps. Privacy or data stewardship appeared in 29 reviews, transparency or explainability in 27, bias or fairness in 25, accountability or liability in 25, consent or autonomy in 18, leadership, education, or oversight in 14, and safety or human oversight in nine. Among 23 reviews with enumerable, extractable study lists, the CCA was 0.54%, indicating slight overlap. Nursing-specific concerns involved professional judgment, representation, oversight, regulatory variation, and ethical preparedness. Conclusion AI creates linked governance concerns involving data, fairness, transparency, autonomy, and accountability. Auditable responsibilities are needed across clinical, institutional, and regulatory levels while preserving nursing judgment and patient advocacy. Evidence for specific regulatory models remains limited.
Daifallah M. Alrazeeni, Maryam Alharrasi, M. K. K. Rony et al.· SAGE Open Nursing· 0 citations
This review examines the integration of causal artificial intelligence (AI) and data-driven decision intelligence within healthcare informatics systems to advance personalized medicine and clinical decision-making. A narrative review methodology was employed, synthesizing interdisciplinary literature from major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Studies focusing on causal inference, decision intelligence, and healthcare informatics applications in personalized medicine were included. Data were extracted on methodological approaches, healthcare settings, analytical techniques, and clinical applications, followed by thematic synthesis. Findings indicate that causal AI enhances clinical decision support by enabling estimation of treatment effects and simulation of intervention outcomes at the individual patient level. Integration of multimodal health data such as electronic health records, genomic data, and real-time monitoring improves prediction accuracy and supports tailored treatment strategies. Additionally, causal models improve interpretability, fostering clinician trust and facilitating transparent decision-making. Robust healthcare informatics infrastructures, including interoperable systems and data warehouses, were identified as critical enablers of causal analytics. Overall, causal AI represents a transformative advancement in healthcare analytics, supporting more informed, individualized, and evidence-based clinical decisions. Its integration within healthcare informatics systems has significant potential to improve patient outcomes and guide the future of intelligent, personalized healthcare delivery.
Mohammed Majid Bakhsh, Emran Hossain, M. K. K. Rony et al.· Personalized Medicine· 0 citations
Findings indicate that artificial intelligence can support the integration and analysis of multi-omics data, support the identification of genetic variants and disease associations, and improve predictive modeling for precision medicine.
Towsif Alam, Koushik Saha, M. K. K. Rony et al.· Journal of Multidisciplinary...· 0 citations
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