Review Article

Trends in Nursing Research on AI-Based Healthcare Environments in South Korea: A Scoping Review

Bok Sil Hong¹, Myoung-Ryu Kim², *

Abstract

Background/Objectives: In the rapidly evolving healthcare landscape, the integration of artificial intelligence (AI) technologies has significantly influenced nursing practice, education, and research. This study aimed to analyze trends in AI-based nursing research conducted in Korea over the past decade (2014–2024), focusing on research design types, AI application areas, and par-ticipant characteristics.

Methods: A scoping review was conducted following the framework proposed by Arksey and O’Malley (2005). Literature searches were performed using domestic and international databases, including RISS, KISS, KCI, CINAHL, and PubMed. Out of 402 initially identified studies, 42 met the inclusion criteria and were analyzed according to publication year, study design, AI application field, and participant characteristics. Descriptive statistics and frequency analyses were used to identify research trends.

Results: Among the 42 reviewed studies, descriptive designs accounted for the largest portion (52.4%), followed by systematic reviews (26.2%) and experimental studies (21.4%). AI technologies were most frequently applied to clinical practice support (40.5%), nursing education (31.0%), and patient monitoring (28.5%). The majority of studies involved nurses (64.3%), while fewer studies targeted nursing students or patients. The findings indicate a growing emphasis on clinical efficiency and patient safety, with increasing attention to ethical decision-making in AI-integrated nursing environments.

Conclusions: AI-based nursing research in Korea remains in an early exploratory stage, dominated by descriptive designs. However, there is a noticeable increase in experimental and systematic review studies. Future research should strengthen methodo-logical rigor and broaden participant diversity, emphasizing ethical responsibility and international collaboration to en-hance the quality and global relevance of AI-based nursing research.

Keywords

Artificial Intelligence, Healthcare Environment, Nursing Research, Nursing Education, Ethical Decision Original Article

Introduction

The use of artificial intelligence (AI) technologies in contemporary healthcare has grown rapidly, and their scope and necessity in nursing are becoming increasingly prominent. This is because AI contributes not only to improving the efficiency of nursing workflows but also to enhancing patient safety and nurses’ job satisfaction. Indeed, numerous studies in Korea and abroad have reported that AI can be effectively utilized for clinical decision support, nursing education, and patient monitoring [1, 2]

In particular, nursing practice faces the risk of reduced efficiency and compromised quality of patient care due to the continuous increase in workload and nurses’ chronic fatigue and stress. Accordingly, AI-driven task automation and support systems are emerging as crucial tools to address these challenges [2]. Moreover, during the recent COVID-19 pandemic, the problem of nursing overload became even more serious, making AI-enabled systems for patient management and monitoring more essential than ever [3].

However, in the Korean nursing field, there is still a lack of systematic and in-depth analytical research on AI applications. Most domestic studies remain at a descriptive level, indicating the need for research that evaluates the practical clinical implementation of AI and its long-term utility [2, 4]. Research on the ethical and legal issues that may arise with AI adoption also remains insufficient, underscoring the urgent need for deeper discussion and inquiry in this area [4].

Therefore, this study aims to systematically analyze the current status and research trends of AI utilization in Korean nursing by employing the scoping review methodology proposed by Arksey and O’Malley [5]. Specifically, we clarify the present landscape of AI-related nursing research in Korea and examine study design types, AI application domains, characteristics of research participants, and ethical issues, with the goal of suggesting future directions for AI-based nursing research.

The findings are expected to provide foundational evidence for establishing effective strategies for AI application in nursing, thereby contributing to improved nursing efficiency and the quality of patient management. Ultimately, these results may serve as practical evidence to enhance the quality of nursing practice and education.

Materials & Methods

Design Framework

This study employed a scoping review to identify trends in domestic nursing research con-ducted within AI-based healthcare environments. We adopted Arksey and O’Malley’s five stage framework for scoping reviews: (1) identifying the research question; (2) iden-tifying relevant studies; (3) study selection; (4) charting the data; and (5) collating, sum-marizing, and reporting. This approach suits emerging, heterogeneous literatures like AI in nursing. Information sources and search strategy

Study Scope and Data Collection

Following the five-stage framework proposed by Arksey and O’Malley (2005), the review proceeded as follows:

• Stage 1: Identifying the research question.
  The guiding question was: “What are the current perceptions and patterns of AI utilization in the Korean field of nursing?”

• Stage 2: Identifying relevant studies.
  Literature searches targeted AI-related studies published in nursing (domestic and international) from 2014 to 2024, using the databases RISS, KISS, KCI, CINAHL, and PubMed.

• Stage 3: Study selection.
  A total of 402 records were identified; after removing 35 duplicates, 367 records were screened by title and abstract. Of these, 295 were excluded, 72 proceeded to full-text review, and 42 studies were finally included [6-47]. The study selection flow is shown in Figure 1

Figure 1. Flow-chat of Study selection process

• Stage 4: Charting and analyzing the data.
  The included studies were analyzed by year of publication, study design, AI application domain, and participant characteristics.

• Stage 5: Collating, summarizing, and reporting the results. Findings were organized and presented using tables and figures.

Results

1. General Characteristics

The general characteristics of the 42 studies included in this review are summarized in Table 1. A majority were published in the last five years (2020–2024), accounting for 73.8% (n= 31), indicating a growing trend in the application of AI technologies within the nursing field. With respect to study design, descriptive studies were most common (52.4%), followed by experimental studies and systematic reviews. Regarding study participants, nurses con-stituted more than half of the samples (64.3%).

Table 1. General Characteristics of Reviewed Studies

Year of Publication

An analysis of the 42 studies included in the final sample showed that 5 studies (11.9%) were published between 2014 and 2016, and 6 studies (14.3%) between 2017 and 2019, indicating a slight increase. Notably, the number rose sharply to 14 studies (33.3%) during 2020–2022, and the most recent period, 2023–2024, accounted for 17 studies (40.5%), the largest share. These trends suggest that interest in and research on the use of AI technologies in the Korean nursing field have become increasingly active in recent years, and continued growth is an-ticipated.

Study Design Types

With respect to study design, descriptive studies constituted the largest proportion, 22 of 42 studies (52.4%), followed by systematic reviews (11 studies; 26.2%) and experimental studies (9 studies; 21.4%). This distribution indicates that research on AI utilization in Ko-rean nursing remains largely at an exploratory stage, highlighting the need for more ex-perimental approaches and rigorous, evidence-based investigations going forward.

Application Domains of AI

Regarding application domains across the 42 studies, clinical practice support was most common (17 studies; 40.5%), followed by nursing education (13 studies; 31.0%) and patient monitoring (12 studies; 28.5%). The prominence of clinical practice support may reflect expectations that AI can help reduce nurses’ workload and improve the quality of nursing services. Although AI use is also increasing in the education domain, empirical evaluations of diverse pedagogical approaches remain at an early stage, indicating a need for expanded research, including careful attention to ethical dimensions.

Characteristics of Study Participants

Analyses of participants showed that studies most frequently targeted nurses (27 studies; 64.3%). This likely reflects the direct impact of AI adoption on clinical nurses’ work efficiency and job satisfaction. Studies with nursing students accounted for 9 studies (21.4%), while those involving patients and caregivers accounted for 6 studies (14.3%). The relatively smaller share of nursing-student-focused research underscores the need for educational approaches that enhance students’ acceptance and competency in AI technologies; likewise, research centered on patients and caregivers should be expanded in the future.

Taken together, these findings indicate a rapid increase in the application of AI technologies with growing implementation in clinical settings in recent years. Future work should include in-depth and experimental studies in nursing education to verify the effectiveness of AI in both practice and education. Moreover, expanding research to encompass a broader range of participants, including nursing students, patients, and caregivers, will be essential to evaluate the comprehensive and multifaceted effects of AI adoption.

2. Differences in Research Trends by General Characteristics

For the 42 studies analyzed, chi-square (χ²) tests were conducted to examine whether research trends differed by study design, AI application domain, and characteristics of study partic-ipants. The detailed results for each category are presented in Table 2.

Table 2. Differences of Research Trends by General Characteristics of Studies

*p<.05, **p<.01

Differences in Research Trends by Study Design

Analysis by study design showed that descriptive studies recorded the highest frequency 22 out of 42 (52.4%) followed by systematic reviews (11; 26.2%) and experimental studies (9; 21.4%). The difference in research trends across study designs was statistically significant (χ² = 7.52, p = .023). This indicates that AI-related research in the Korean nursing field remains primarily exploratory and descriptive, while the proportions of systematic reviews and ex-perimental studies are gradually increasing. It further suggests the need for more in-depth experimental work and evidence-based systematic reviews moving forward.

Differences in Research Trends by AI Application Domain

With respect to AI application domains, clinical practice support showed the highest pro-portion (40.5%, n = 17), followed by education (31.0%, n = 13) and patient monitoring (28.5%, n = 12). Differences in research trends across application domains were statistically significant (χ² = 6.11, p = .047). These findings suggest that AI research aimed at supporting nursing tasks in clinical settings has been relatively more active than in other domains, re-flecting expectations for improved efficiency and quality of nursing services. Although re-search in the education domain is increasing, further studies are needed to develop effective AI-enhanced educational programs and evaluate their real-world implementation, including ethical considerations.

Differences in Research Trends by Participant Characteristics

By participant characteristics, studies most frequently targeted nurses (27; 64.3%), followed by nursing students (9; 21.4%) and patients/caregivers (6; 14.3%). Differences in research trends across participant groups were highly significant (χ² = 0.52, p = .005). This suggests that research has mainly focused on directly evaluating the feasibility and effects of AI adoption in clinical nursing practice, with an emphasis on improving nurses’ work environments and efficiency. Although studies involving nursing students and patients/caregivers accounted for smaller proportions, expanding research with these groups is important for assessing the effectiveness of AI-based nursing interventions and education.

Taken together, these results indicate that AI-focused research in the Korean nursing field shows statistically significant differences by study design, application domain, and participant characteristics, thereby underscoring the need for more balanced and methodologically diverse investigations Future research agendas should aim for greater diversity in study designs and a more balanced emphasis on nursing education and patient-centered studies. Overall, the growing body of work, especially in clinical practice support, suggests that AI is being applied chiefly to clinical decision support. The comparatively lower proportion of experimental studies than descriptive ones also confirms that AI adoption remains at an early exploratory stage. Strengthening experimental designs and ethical investigations will be essential to rigorously evaluate the long-term effectiveness and safety of AI technologies.

Discussions

This study analyzed trends in artificial intelligence (AI)–based research conducted in the Korean nursing field over the past decade (2014–2024) using the scoping review methodology proposed by Arksey and O’Malley [5]. By classifying 42 articles according to study design, AI application domain, and participant characteristics, we identified significant differences in research trends across these characteristics. Although the introduction and study of AI in Korean nursing remain relatively early-stage, there has been a recent tendency toward greater diversity in study designs, application areas, and participant characteristics [18].

With respect to study design, descriptive studies showed the highest frequency. This indicates that applications of AI are in an exploratory phase, with a primary focus on situational assessment and feasibility exploration [48]. Internationally, early AI research has been reported to transition from predominantly technical approaches to experimental approaches, with increasing numbers of systematic reviews and meta-analyses [15]. Accordingly, in Korea as well, AI-based nursing research needs to adopt more experimental and rigorous designs and to promote systematic and in-depth literature reviews [21].

The domain with the highest proportion of AI application was clinical practice support. AI technologies are being actively introduced in clinical settings with the aim of improving nursing efficiency and reducing workload, and recent studies have reported that AI-based monitoring systems and nursing decision-support systems are effective in enhancing the quality of nursing work [14]. These findings suggest that AI has the potential to contribute to efficiency and quality improvement in nursing services [18]. In particular, numerous studies have reported that AI-based patient monitoring and decision-support systems are effective in improving clinical judgment and accuracy among nurses [22]. However, it must be emphasized that AI cannot replace nurses’ ethical judgment and professional responsibility; therefore, the development and implementation of educational programs to strengthen nurses’ ethical decision-making capabilities are essential [13].

The application of AI in nursing education has also been active, showing rapid growth in recent years. Domestic and international studies support the effectiveness of simulation and virtual reality (VR)–based education incorporating AI in improving students’ clinical judgment and ethical decision-making competencies [49]. Going forward, continuous development and evaluation of such AI-based educational programs are needed to enhance educational quality while simultaneously strengthening nurses’ ethical judgment.

In the analysis of participant characteristics, studies targeting nurses accounted for the largest proportion. This reflects the increasing clinical utility of AI technologies and the importance of nurses’ roles in real-world settings [25]. By contrast, studies targeting nursing students or patients and caregivers remain relatively insufficient; thus, research including these groups should be expanded to more comprehensively verify the effects of AI application [50].

Ethical decision-making has emerged as an important theme in the adoption of AI technologies. As AI systems are more widely implemented, the ethical issues faced by nurses will likewise increase, making educational support to strengthen ethical decision-making indispensable [25, 49, 50] .Therefore, there is a need to develop systematic educational programs that integrate AI use with ethical considerations.

This study systematically analyzed trends in AI-based nursing research in Korea, elucidating study designs, application domains, and participant characteristics. However, it is limited by its restriction to domestic literature, which constrains international generalizability, and by the absence of a formal quality appraisal. Future studies should include in-depth meta-analyses encompassing international research as well as rigorous quality assessments.

As AI use increases, issues of nurses’ ethical judgment and responsibility are becoming even more important; difficulties related to ethical responsibility and decision-making have been reported in clinical practice. At the same time, studies have reported that AI-enabled nursing education can improve students’ ethical decision-making [16]. Accordingly, strengthening ethical decision-making competence should be established as an essential educational goal in AI-based nursing education.

In future directions, AI-based nursing research should encompass diverse study designs and participant characteristics and address ethical decision-making in an integrated manner. In doing so, it can be expected to contribute not only to the development of nursing research but also to improving the quality of nursing services in actual clinical settings.

Conclusions

This study analyzed trends in AI-based research conducted in the Korean nursing field over the past decade (2014–2024) using Arksey and O’Malley’s (2005) scoping review methodology. Overall, AI research in Korean nursing remains at an early exploratory stage, with a predominance of descriptive studies; however, there is a recent shift toward experimental designs and systematic reviews.

AI technologies have contributed notably to improving nurses’ clinical workflow efficiency and patient safety, and they have gained attention as innovative approaches in nursing education. Nevertheless, study samples have been disproportionately centered on nurses. Future research should broaden its scope to include nursing students, patients, and caregivers in order to evaluate the effects of AI more comprehensively.

As AI adoption expands, nurses’ ethical decision-making becomes increasingly critical. It is therefore essential to cultivate ethical decision-making competencies in both AI-based nursing education and clinical practice, and to advance research that emphasizes ethical responsibility and human-centered approaches.

In conclusion, the domestic AI nursing research agenda should pursue a balanced advancement that includes a transition toward experimental and systematic study designs, diversification of participant groups, and the strengthening of ethical decision-making competencies. Continued international-level studies and comparative analyses are needed to sustain an in-depth discourse on the effective application of AI technologies within the nursing discipline.

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