Artificial Intelligence and Machine Learning in Nursing: Transforming Clinical Practice and Professional Development.
Artificial intelligence (Al) and machine learning (ML) are increasingly integrated into healthcare systems, with significant implications for nursing practice. However, evidence regarding effectiveness, implementation challenges, and impact on nursing outcomes remains fragmented. This narrative revi...
| Publicado en: | Nursing Journal of India Vol. 117; no. 3; pp. 99 - 104 |
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| Autores principales: | , |
| Formato: | review Journal Article |
| Publicado: |
Trained Nurses Association of India
May/Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196631675&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196631675 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00296503 OQW jtl: Nursing Journal of India issn: 00296503 maglogo: N pubinfo: dt: May/Jun2026 vid: 117 iid: 3 pid: 15537 pub: Trained Nurses Association of India artinfo: ui: 196631675 196631675 196631675 196631675 ppf: 99 ppct: 5 formats: tig: atl: Artificial Intelligence and Machine Learning in Nursing: Transforming Clinical Practice and Professional Development. aug: au: Reena, N. Selvi, M. Sankara affil: Principal, College of Nursing, Government Medical College, Azamgarh district (UP) sug: subj: Artificial Intelligence Machine Learning Nursing Practice Professional Development Implementation Science Nursing Staff, Hospital Nurse Educators Policy Making Administrative Personnel Decision Making Prediction Algorithms ab: Artificial intelligence (Al) and machine learning (ML) are increasingly integrated into healthcare systems, with significant implications for nursing practice. However, evidence regarding effectiveness, implementation challenges, and impact on nursing outcomes remains fragmented. This narrative review, using PubMed, CINAHL, Scopus, and IEEE Xplore (from Jan 2020-Dec 2025), examines AI/ ML applications in nursing practice, analyses implementation challenges, evaluates clinical effectiveness evidence, and identifies research gaps. Search terms combined 'artificial intelligence,' 'machine learning,' nursing,' and 'clinical decision support.' Inclusion: peer-reviewed empirical studies examining AI/ ML in nursing. Exclusion: theoretical papers, nonnursing studies. Analysis employed GRADE criteria for evidence quality assessment; 68 studies demonstrated AI/ ML applications across predictive analytics, clinical decision support, intelligent monitoring, documentation, and education. Critical limitations included small samples, short term follow-up, limited RCTs, and algorithmic bias affecting underrepresented populations. Evidence quality was predominantly moderate. While AI/ ML demonstrate technical capability, evidence for sustained clinical effectiveness remains limited by methodological weaknesses, implementation challenges, and insufficient examination of nursing-sensitive outcomes. Critical engagement, rigorous evaluation, and attention to equity are essential. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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