TY - JOUR
T1 - Artificial intelligence in nursing and midwifery
T2 - A systematic review
AU - O'Connor, Siobhán
AU - Yan, Yongyang
AU - Thilo, Friederike J. S.
AU - Felzmann, Heike
AU - Dowding, Dawn
AU - Lee, Jung Jae
N1 - Publisher Copyright:
© 2022 John Wiley & Sons Ltd.
PY - 2023/7
Y1 - 2023/7
N2 - BackgroundArtificial Intelligence (AI) techniques are being applied in nursing and midwifery to improve decision-making, patient care and service delivery. However, an understanding of the real-world applications of AI across all domains of both professions is limited.ObjectivesTo synthesise literature on AI in nursing and midwifery.MethodsCINAHL, Embase, PubMed and Scopus were searched using relevant terms. Titles, abstracts and full texts were screened against eligibility criteria. Data were extracted, analysed, and findings were presented in a descriptive summary. The PRISMA checklist guided the review conduct and reporting.ResultsOne hundred and forty articles were included. Nurses’ and midwives' involvement in AI varied, with some taking an active role in testing, using or evaluating AI-based technologies; however, many studies did not include either profession. AI was mainly applied in clinical practice to direct patient care (n = 115, 82.14%), with fewer studies focusing on administration and management (n = 21, 15.00%), or education (n = 4, 2.85%). Benefits reported were primarily potential as most studies trained and tested AI algorithms. Only a handful (n = 8, 7.14%) reported actual benefits when AI techniques were applied in real-world settings. Risks and limitations included poor quality datasets that could introduce bias, the need for clinical interpretation of AI-based results, privacy and trust issues, and inadequate AI expertise among the professions.ConclusionDigital health datasets should be put in place to support the testing, use, and evaluation of AI in nursing and midwifery. Curricula need to be developed to educate the professions about AI, so they can lead and participate in these digital initiatives in healthcare.Relevance for clinical practiceAdult, paediatric, mental health and learning disability nurses, along with midwives should have a more active role in rigorous, interdisciplinary research evaluating AI-based technologies in professional practice to determine their clinical efficacy as well as their ethical, legal and social implications in healthcare.
AB - BackgroundArtificial Intelligence (AI) techniques are being applied in nursing and midwifery to improve decision-making, patient care and service delivery. However, an understanding of the real-world applications of AI across all domains of both professions is limited.ObjectivesTo synthesise literature on AI in nursing and midwifery.MethodsCINAHL, Embase, PubMed and Scopus were searched using relevant terms. Titles, abstracts and full texts were screened against eligibility criteria. Data were extracted, analysed, and findings were presented in a descriptive summary. The PRISMA checklist guided the review conduct and reporting.ResultsOne hundred and forty articles were included. Nurses’ and midwives' involvement in AI varied, with some taking an active role in testing, using or evaluating AI-based technologies; however, many studies did not include either profession. AI was mainly applied in clinical practice to direct patient care (n = 115, 82.14%), with fewer studies focusing on administration and management (n = 21, 15.00%), or education (n = 4, 2.85%). Benefits reported were primarily potential as most studies trained and tested AI algorithms. Only a handful (n = 8, 7.14%) reported actual benefits when AI techniques were applied in real-world settings. Risks and limitations included poor quality datasets that could introduce bias, the need for clinical interpretation of AI-based results, privacy and trust issues, and inadequate AI expertise among the professions.ConclusionDigital health datasets should be put in place to support the testing, use, and evaluation of AI in nursing and midwifery. Curricula need to be developed to educate the professions about AI, so they can lead and participate in these digital initiatives in healthcare.Relevance for clinical practiceAdult, paediatric, mental health and learning disability nurses, along with midwives should have a more active role in rigorous, interdisciplinary research evaluating AI-based technologies in professional practice to determine their clinical efficacy as well as their ethical, legal and social implications in healthcare.
KW - artificial intelligence
KW - deep learning
KW - healthcare
KW - machine learning
KW - midwifery
KW - natural language processing
KW - neural networks
KW - nursing
UR - http://www.scopus.com/inward/record.url?scp=85135138042&partnerID=8YFLogxK
U2 - 10.1111/jocn.16478
DO - 10.1111/jocn.16478
M3 - Review article
C2 - 35908207
AN - SCOPUS:85135138042
SN - 0962-1067
VL - 32
SP - 2951
EP - 2968
JO - Journal of Clinical Nursing
JF - Journal of Clinical Nursing
IS - 13-14
ER -
