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.

This study aimed was done to analyze recent trends in nursing research published in the Journal of Korean Academy of Nursing by focusing on the content of nursing interventions and their level of evidence. A total of 209 studies published between 2007 and 2008 were reviewed using analysis criteria developed by the researchers. The number of quantitative studies was greater than that of qualitative studies. There was a slight increase in the number of qualitative studies and studies including elderly populations, which reflects the recent population trend in Korea. More randomized controlled trials with a low risk of bias were needed to support more evidence-based nursing practice. Concerning the low rate of ethical consideration, stricter application of research ethics needs to be encouraged. The findings of this study suggest the recent trends in nursing research and the direction of nursing research and review in the Journal.

Jeong GH, et al. Korean J Women Health Nurs. 2009 Dec;15(4):261-269. http://dx.doi.org/10.4069/kjwhn.2009.15.4.261

We hypothesized that it is not open access status but rather exposure in the PubMed platform that affects citation frequency in medical journals. In November 2008, medical journals from Korea began to be added to PubMed Central (PMC). Therefore, it would be interesting to know whether or not their impact factor based on Web of Science has increased since the journals were listed in PMC/PubMed. To answer this question, a citation analysis of seven journals that have been indexed in PMC since 2008 or 2009 was done. Only non-Medline journals were selected and the impact factors of five Medline journals were compared. The impact factor was calculated via Web of Science. Journal Citation Reports (JCR) data were used if the data of the target journals were provided in JCR. Trends for the impact factors of different years were analyzed using dBSTAT ver. 5.0. There has been an increasing rates of the impact factor for the seven non-Medline journals, 1.92 in 2011 over 2010 ; 3.27 in 2010 over 2009 ; 1.12 in 2012 over 2011. As for the five Medline journals, the increasing rate in 2010 over 2009 was 1.18 ; however, those of 2011 over 2010 and 2012 over 2011 were 1.01 and 1.04 respectively. The Science Citation Index Expanded impact factor of medical journals published in Korea can be increased if those journals are published in English and listed in PMC. This is an effect of the platform in which the journals are listed and not just an effect of free access

Kim KM, et al. J Korean Acad Nurs Adm. 2015 Dec;21(5):542-551. http://dx.doi.org/10.11111/jkana.2015.21.5.542