Artificial intelligence, machine learning, and deep learning in women’s health nursing
Open accessAbstract
Artificial intelligence (AI), which includes machine learning and deep learning has been introduced to nursing care in recent years. The present study reviews the following topics: the concepts of AI, machine learning, and deep learning; examples of AI-based nursing research; the necessity of education on AI in nursing schools; and the areas of nursing care where AI is useful. AI refers to an intelligent system consisting not of a human, but a machine. Machine learning refers to computers' ability to learn without being explicitly programmed. Deep learning is a subset of machine learning that uses artificial neural networks consisting of multiple hidden layers. It is suggested that the educational curriculum should include big data, the concept of AI, algorithms and models of machine learning, the model of deep learning, and coding practice. The standard curriculum should be organized by the nursing society. An example of an area of nursing care where AI is useful is prenatal nursing interventions based on pregnant women's nursing records and AI-based prediction of the risk of delivery according to pregnant women's age. Nurses should be able to cope with the rapidly developing environment of nursing care influenced by AI and should understand how to apply AI in their field. It is time for Korean nurses to take steps to become familiar with AI in their research, education, and practice.
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In a prior study, participants' perceived usefulness scores for ChatGPT distinctly exceeded the median value.
“These positive findings echo the results of a prior study where participants' perceived usefulness scores for ChatGPT distinctly exceeded the median value.30”
From The Impact of Artificial Intelligence-Assisted Learning on Nursing Students' Ethical Decision-making and Clinical Reasoning in Pediatric Care (2024) · cites this paper as [30]
The section ‘In which nursing research areas has AI been adopted?’ describes a mental-health chatbot named Tess and other nursing AI applications, but gives no ChatGPT perceived-usefulness scores. Searches for ChatGPT, perceived usefulness, and a median score found no passage addressing the claimed result. (LLM, conf=0.78)
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- Authors
- Jeong, G. H.
- Affiliations
- Hallym University (KR)
- Corresponding author
- Jeong, Geum Hee
- ORCID
- Jeong, Geum Hee
- Venue
- Korean Journal of Women Health Nursing
- Published
- 2020-03-31
- Volume
- 26
- Issue
- 1
- Pages
- 5-9
- ISSN
- 1225-9543 (ISSN-L)
- 2287-1640
- 2093-7695
- PMC
- PMC9334197
- OpenAlex
- W3016716625
- Cited by
- 51
- References
- 10
- Citations by year
- 2026: 6 · 2025: 20 · 2024: 14 · 2023: 6 · 2022: 2 · 2021: 2 · 2020: 1
- FWCI
- 0.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Korean journal of women health nursing/Yeoseong geon'gang ganho hag'hoeji/Yeoseong geon-gang ganho hakoeji (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, machine learning, deep learning, women's health nursing, nursing education, AI curriculum, artificial neural networks, prenatal nursing, nursing records, big data
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