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J Clinic Care Skill 2024, 5(4): 215-224 |
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Received: 2024/09/25 | Accepted: 2024/10/31 | Published: 2024/11/5
Received: 2024/09/25 | Accepted: 2024/10/31 | Published: 2024/11/5
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Seyed-Nezhad M, Yousefianzadeh O, Mirzaee M, Moradi-Joo M. Potential of Artificial Intelligence in Improvement of the Clinical, Educational, Decision-Making, Information and Research Skills of Nurses. J Clinic Care Skill 2024; 5 (4) :215-224
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1- National Center for Health Insurance Research, Tehran, Iran
2- “Department of Health Information Technology, School of Public Health” and “Health Technology Assessment & Medical Informatic Research Center”, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
3- Department of Internal Surgery, School of Nursing and Midwifery, Yasuj University of Medical Sciences, Yasuj, Iran
4- Social Determinants of Health Research Center, Yasuj University of Medical Sciences, Yasuj, Iran
2- “Department of Health Information Technology, School of Public Health” and “Health Technology Assessment & Medical Informatic Research Center”, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
3- Department of Internal Surgery, School of Nursing and Midwifery, Yasuj University of Medical Sciences, Yasuj, Iran
4- Social Determinants of Health Research Center, Yasuj University of Medical Sciences, Yasuj, Iran
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Introduction
The healthcare sector is currently experiencing a profound transformation driven by technological advancements, particularly in artificial intelligence (AI). AI has demonstrated significant potential to enhance various facets of healthcare delivery, including diagnostics, treatment personalization, and patient management [1]. However, the application of AI in nursing, which constitutes a critical component of healthcare, remains relatively underexplored. Nurses play a pivotal role in clinical, educational, managerial, and decision-making processes, yet they face numerous challenges such as information overload, complex decision-making scenarios, and the continuous need for professional development [2, 3]. These challenges necessitate innovative solutions to improve the efficiency and effectiveness of nursing practice.
Despite the promising advancements of AI in general healthcare, there is a paucity of comprehensive reviews specifically addressing how AI can enhance nursing skills. Current literature has primarily focused on AI's applications in medicine at large, with limited exploration of its potential to support nurses in their multifaceted roles [4, 5]. Furthermore, most studies that do consider AI in nursing tend to concentrate on a single aspect, such as clinical decision support or administrative tasks, without providing a holistic view of AI's potential benefits across the spectrum of nursing responsibilities [6, 7].
Addressing this gap is crucial, as nurses are at the forefront of patient care and their ability to effectively utilize AI could significantly impact patient outcomes and healthcare delivery. Therefore, this manuscript aims to explore the potential of AI to improve the clinical, educational, managerial, decision-making, information, and research skills of nurses, offering a comprehensive review of the scope and highlighting areas for future research and development.
The application of AI in healthcare has been a subject of considerable research interest, with numerous studies highlighting its potential benefits. In clinical settings, AI has demonstrated the ability to improve diagnostic accuracy, predict patient outcomes, and personalize treatment plans [1, 8]. For instance, AI algorithms can analyze large volumes of medical data to identify patterns that may not be evident to human clinicians, thereby aiding in early diagnosis and intervention [4].
In the realm of nursing, the integration of AI has shown promise in several areas. Clinical decision support systems (CDSS) powered by AI can assist nurses in making informed decisions by providing real-time data analysis and evidence-based recommendations [2]. These systems can reduce the cognitive load on nurses, allowing them to focus more on patient care. Furthermore, AI can enhance educational outcomes by providing personalized learning experiences tailored to the individual needs of nursing students and practitioners [7].
Managerially, AI can streamline administrative tasks such as scheduling, resource allocation, and workflow optimization, thereby improving operational efficiency [5]. In decision-making processes, AI can support nurses by offering predictive analytics and risk assessments, enabling proactive rather than reactive interventions [6].
AI's role in information management is also noteworthy. It can facilitate the organization, retrieval, and dissemination of vast amounts of medical information, making it more accessible and usable for nurses [3]. Moreover, AI can aid in research by automating data collection and analysis, thus accelerating the pace of nursing research and innovation [1].
Although extensive research has been conducted to examine the role of AI in improving nurses’ clinical skills, the literature lacks a comprehensive approach. Therefore, there has been no comprehensive and focused study that highlights all the roles of AI in improving nursing skills. Most studies have either focused on a specific aspect of nursing or have generally discussed the impact of AI on healthcare without addressing its implications for nursing practice. This review aims to fill this gap and provide a detailed review of the scope of AI in improving nurses’ skills, providing a comprehensive understanding of how AI can be utilized to address the challenges faced by nurses and improve their overall efficiency and effectiveness. Therefore, this scoping review was conducted with the aim of determining the potential of AI in improving nurses’ clinical, educational, decision-making, informational, and research skills.
Information and Methods
This scoping review was conducted based on the framework of Arksey & O'Malley [9] in 2005 and using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews) guidelines [10].
Search strategy
The authors developed search strategies with a medical librarian to identify published reviews on the role of AI in improving nurse education, practice, and clinical skills. Using a combination of keywords, text phrases, Medical Subject Headings (MeSH) and other database-specific terms, strategies were developed to find relevant review articles. Eight electronic databases (PubMed, Scopus, Web of Science, Embase, CINAHL, ProQuest, Microsoft Academic, and OpenGrey) were searched to find all studies (peer-reviewed and grey literature) published up to September 2024. The Google Scholar search engine was used to find relevant references and complete the search coverage. The reference lists of the retrieved articles were also used to find relevant articles. Relevant studies found were screened independently by two investigators.
Keywords used were “Nurse*”, “Nursing Care”, “Nursing Care Plan”, “Nursing Workflows”, “Patient Care”, “Patient Care Planning”, “Patient Monitoring”, “Patient Diagnosis”, “Artificial Intelligence”, “AI”, “Machine Learning”, “Expert Systems”, “Neural Networks”, “Computer”, “Natural Language Processing”, “Fuzzy Logic”, “Data Mining”, “Decision-making”, “Computer-assisted”, “Decision Support Techniques”, “Computer Vision System*”, “Advanced technologies in AI”, “Transfer Learning”, “Support Vector Machine”, “Deep Learning”, “Neural Network*”, “Data Mining” and “Bayesian Network*”.
Selection and inclusion criteria
First, the studies obtained from the electronic and manual search were organized using EndNote software, and then the screening and selection of studies were carried out in two stages based on the inclusion and exclusion criteria. In the first stage, after removing duplicate articles, the titles and abstracts of the articles were reviewed, and in the second stage, the full text of the selected articles was collected and reviewed. At the end of this stage, the reference lists of the remaining articles were reviewed and, if necessary, the responsible authors of the important articles were contacted. Finally, the search and selection process of studies was drawn using the PRISMA flow chart.
Inclusion criteria
- Articles published in Persian and English
- Review articles (review, narrative review, rapid review, scoping review and systematic review)
- Articles related to nursing care
- Journal articles
- Articles that addressed the benefits of AI
Exclusion criteria
- Studies that were not specifically related to the role of AI in improving nurses' skills
- Studies outside the health system
- Studies published in a language other than English and Persian
Quality assessment
The quality assessment of the studies was carried out independently by two authors. Disagreements between the two authors were resolved by a third author through consensus. Finally, if necessary, a consensus table and calculation of the kappa statistic were used.
Data extraction and analysis
For data extraction, a special form was designed in Excel 2013 and the required information, including general information (including study title, year of publication, journal, country, first author) and specific information (including objective, type of study, findings) was extracted. Data collected from included studies on each role that AI could play in nurses' skills were analyzed using narrative methods.
Findings
By searching databases and applying a specific search strategy for each database, 284 studies were identified in the first stage, then by manual searching and reviewing the grey literature, 63 studies were added to this number, which resulted in a total of 347 studies being identified. After removing similar titles, 321 studies were subjected to initial screening. After studying the title and abstract, 175 studies were excluded because they were not relevant to the study objective. Therefore, 146 studies were selected and reviewed. 63 studies met the eligibility criteria for this study, of which 30 were selected and analyzed (Figure 1).
Figure 1. Flow diagram for selection of studies (PRISMA diagram)
30 reviewed studies were conducted from 2020 to 2025 exploring the role of AI in nursing and healthcare. Key objectives include improving education, clinical care, efficiency, and examining AI's challenges and opportunities. The studies highlight the benefits, limitations, and future applications of AI in nursing (Table 1).
Table 1. Characteristics of the included studies
The publication year of the included studies was from 2020 to 2025. Out of the 30 included studies, 15 studies (50%) were published in 2024 (Figure 2).
Figure 2. Year of publication of included studies
Out of the 30 reviewed studies, 14 were scoping reviews (Figure 3).
Figure 3. Study type of included studies
The findings of the studies included in this review according to the categories of themes and sub-themes. The findings of the studies included in this review were categorized into six main themes (education, decision, clinical practice, research, information, and psychiatric nursing; Table 2).
Table 2. Findings of the included studies by themes and sub-themes
Discussion
The purpose of this scoping review was to summarize the findings of review articles published over the past years. The results of our study showed that AI plays a fundamental role in improving the clinical, educational, decision-making, informational, and research skills of nurses. AI (AI) is increasingly becoming a transformative force across various sectors, and nursing is no exception. When considering the potential of AI to enhance clinical, educational, decision-making, information, and research skills among nurses, it’s essential to explore multiple perspectives on this topic.
In education, AI can provide interactive learning experiences through simulations and virtual reality scenarios that mimic real-life situations nurses may face. This hands-on approach helps in honing critical thinking and decision-making skills without putting patients at risk [14]. On the flip side, there may be apprehensions about over-reliance on technology in training environments students might miss out on developing essential interpersonal skills that are crucial for effective nursing practice. Although AI has potential benefits in nursing education, there are still some objections to the development of AI. Writing article and theses is crucial for the success of nursing students and practicing nurses, yet preparing a research paper and thesis is a difficult and daunting task even for experienced writers [16]. Therefore, integrating nursing education, practice, and research with AI has both strengths and limitations. Educational educators and nurses should embrace this technology with a positive view and avoid prohibiting its use. In practice, educators should critically evaluate and use AI appropriately to avoid overreliance. In addition, universities, educational institutions or educational departments should develop and implement guidelines and regulations regarding the proper use of AI in nursing education.
AI serves as a powerful tool for enhancing decision-making processes by providing evidence-based recommendations derived from comprehensive data analysis. For instance, clinical decision support systems (CDSS) can alert nurses about potential medication interactions or deviations from best practices based on current guidelines [12, 21]. One significant advantage of AI in nursing is its ability to enhance clinical decision-making. AI algorithms can analyze vast amounts of patient data quickly, providing nurses with evidence-based recommendations for care [15]. For instance, predictive analytics can help identify patients at risk for complications, allowing nurses to intervene proactively [17, 23]. This not only improves patient outcomes but also empowers nurses by giving them access to advanced tools that augment their clinical judgment [14]. However, there is a risk that such systems could inadvertently contribute to "alert fatigue", where constant notifications lead to desensitization among nursing staff.
From a clinical standpoint, AI can significantly improve patient outcomes through predictive analytics and personalized medicine [29]. For example, AI algorithms can analyze vast amounts of patient data to identify at-risk patients early or suggest tailored treatment plans based on individual health histories. This capability allows nurses to focus more on direct patient care rather than administrative tasks, thereby improving their efficiency and effectiveness [17, 38]. However, there are concerns regarding the reliability of AI systems; if not properly validated, these tools could lead to misdiagnoses or inappropriate treatments.
From a managerial perspective, AI can optimize staffing schedules and resource allocation by analyzing patterns in patient flow and care requirements [15]. This optimization leads to a better work-life balance for nurses and reduced burnout, which is an important issue in the healthcare sector. One of the most compelling arguments for AI in nursing is its ability to automate routine tasks [36]. For instance, AI can assist with scheduling, patient triage, and data entry, allowing nurses to devote more time to direct patient care [12]. By streamlining administrative duties, nurses can focus on critical thinking and clinical judgment skills that are vital for improving patient outcomes [14]. Furthermore, AI algorithms can analyze vast amounts of patient data quickly, providing insights that can inform decision-making processes [17, 20]. This capability not only enhances efficiency but also promotes evidence-based practices. Nonetheless, some argue that excessive reliance on algorithms for decision-making could undermine human judgment and intuition which are vital in complex healthcare settings.
In terms of information management, AI technologies like natural language processing (NLP) can streamline documentation processes by transcribing notes directly into electronic health records (EHRs). This not only saves time but also ensures more accurate records [40]. Conversely, privacy concerns arise with increased digitalization; Safeguarding sensitive patient information becomes paramount as cyber threats evolve.
In the realm of research, AI can streamline data collection and analysis processes. With machine learning algorithms, nurses can conduct more sophisticated analyses of clinical data sets, leading to innovative findings that might have been overlooked through traditional methods [18, 20]. AI also facilitates advanced data analysis techniques that allow for deeper insights into healthcare trends and outcomes over time [41]. By automating literature reviews or synthesizing large datasets quickly, AI empowers nurses engaged in research initiatives to make informed decisions faster than traditional methods would allow [42]. This capability could significantly enhance the quality and quantity of nursing research, ultimately contributing to evidence-based practices that improve patient care on a broader scale. Yet skepticism remains regarding the interpretation of results generated by algorithms researchers must remain vigilant about biases embedded within the data used for training these models.
There is a consensus that AI will not directly replace nurses in the near future. However, it is anticipated that new nurse-patient interactions involving AI will emerge in clinical practice that may enhance nursing performance and the delivery of safe, high-quality care [38]. A review by Ventura-Silva et al. found that the benefits of using AI in nursing care include improved efficiency, decision support and diagnostic accuracy, enhanced interaction and efficient communication, logistical support, reduced workload, and continuous professional development [12]. However, no longitudinal study has been conducted in this area. Therefore, given the lack of useful findings on the effectiveness and efficiency of AI-related tools in real-world scenarios, future research should reflect on the nursing care perspective more than the goals, outcomes, and benefits.
Despite the promising advantages of AI in nursing efficiency, there are notable challenges that must be addressed. One concern is the potential for over-reliance on technology; If nurses become too dependent on AI recommendations without applying their critical thinking skills, it could undermine their professional judgment. In addition, issues of data privacy, legal issues, data security, ethics and resistance to change are of paramount importance as sensitive patient information is managed by these systems [29]. The ethical implications surrounding consent and accountability must also be thoroughly examined as we integrate AI into healthcare practices.
While AI can help with adapting to dynamic situations, critical thinking, patient advocacy, and collaborative efforts, it lacks the emotional intelligence and understanding needed in nursing [29]. Therefore, AI should complement, not replace, healthcare professionals and maintain the essential human element of care. Thus, the unique skills and empathetic aspects of nursing care should be preserved [29, 31].
Limitations
The comprehensiveness of this review led the researchers to select only studies that were fully relevant to the topic based on the title and abstract, so it is possible that a study mentioned nursing in the text and was not included in this review. Another limitation was that we only reviewed articles published in English. Therefore, it is possible that this review missed some relevant articles. There are variations in the use of AI in different settings and countries. This limits generalizability because resources and infrastructure are not the same in different settings and countries. Another limitation was that all relevant articles were published within the last five years, so there is a lack of longitudinal data in this area to assess the lasting impact of AI on nursing care.
This scoping review underscores the significant potential of AI in enhancing various facets of nursing practice. By improving clinical skills, educational methods, decision-making capabilities, and information retrieval, AI stands to revolutionize how nurses operate in today’s healthcare environment. The integration of AI technologies not only fosters better patient outcomes but also equips nurses with the tools necessary for continuous professional development and efficiency. Future research should continue to explore specific applications and address any barriers to implementation to fully realize the benefits AI can bring to the nursing profession.
Conclusion
AI plays a fundamental role in improving the clinical, educational, decision-making, informational, and research skills of nurses.
Acknowledgments: We gratefully acknowledge deputy of research and technology, Yasuj University of Medical Sciences for the support.
Ethical Permissions: This study has been approved by the Research Ethics Committee of the Yasuj University of Medical Sciences (YUMS). The Ethics Code: IR.YUMS.REC.1403.134.
Conflicts of Interests: The authors declare no potential conflict of interest.
Authors' Contribution: Seyed-Nezhad M (First Author), Introduction Writer/Methodologist/Main Researcher/Discussion Writer/Statistical Analyst (28%); Yousefianzadeh O (Second Author), Introduction Writer/Methodologist (17%); Mirzaee MS (Third Author), Discussion Writer/Statistical Analyst (13%); Moradi-Joo M (Fourth Author), Introduction Writer/Methodologist/Main Researcher/Discussion Writer/Statistical Analyst (42%)
Funding/Support: Not applicable.
The healthcare sector is currently experiencing a profound transformation driven by technological advancements, particularly in artificial intelligence (AI). AI has demonstrated significant potential to enhance various facets of healthcare delivery, including diagnostics, treatment personalization, and patient management [1]. However, the application of AI in nursing, which constitutes a critical component of healthcare, remains relatively underexplored. Nurses play a pivotal role in clinical, educational, managerial, and decision-making processes, yet they face numerous challenges such as information overload, complex decision-making scenarios, and the continuous need for professional development [2, 3]. These challenges necessitate innovative solutions to improve the efficiency and effectiveness of nursing practice.
Despite the promising advancements of AI in general healthcare, there is a paucity of comprehensive reviews specifically addressing how AI can enhance nursing skills. Current literature has primarily focused on AI's applications in medicine at large, with limited exploration of its potential to support nurses in their multifaceted roles [4, 5]. Furthermore, most studies that do consider AI in nursing tend to concentrate on a single aspect, such as clinical decision support or administrative tasks, without providing a holistic view of AI's potential benefits across the spectrum of nursing responsibilities [6, 7].
Addressing this gap is crucial, as nurses are at the forefront of patient care and their ability to effectively utilize AI could significantly impact patient outcomes and healthcare delivery. Therefore, this manuscript aims to explore the potential of AI to improve the clinical, educational, managerial, decision-making, information, and research skills of nurses, offering a comprehensive review of the scope and highlighting areas for future research and development.
The application of AI in healthcare has been a subject of considerable research interest, with numerous studies highlighting its potential benefits. In clinical settings, AI has demonstrated the ability to improve diagnostic accuracy, predict patient outcomes, and personalize treatment plans [1, 8]. For instance, AI algorithms can analyze large volumes of medical data to identify patterns that may not be evident to human clinicians, thereby aiding in early diagnosis and intervention [4].
In the realm of nursing, the integration of AI has shown promise in several areas. Clinical decision support systems (CDSS) powered by AI can assist nurses in making informed decisions by providing real-time data analysis and evidence-based recommendations [2]. These systems can reduce the cognitive load on nurses, allowing them to focus more on patient care. Furthermore, AI can enhance educational outcomes by providing personalized learning experiences tailored to the individual needs of nursing students and practitioners [7].
Managerially, AI can streamline administrative tasks such as scheduling, resource allocation, and workflow optimization, thereby improving operational efficiency [5]. In decision-making processes, AI can support nurses by offering predictive analytics and risk assessments, enabling proactive rather than reactive interventions [6].
AI's role in information management is also noteworthy. It can facilitate the organization, retrieval, and dissemination of vast amounts of medical information, making it more accessible and usable for nurses [3]. Moreover, AI can aid in research by automating data collection and analysis, thus accelerating the pace of nursing research and innovation [1].
Although extensive research has been conducted to examine the role of AI in improving nurses’ clinical skills, the literature lacks a comprehensive approach. Therefore, there has been no comprehensive and focused study that highlights all the roles of AI in improving nursing skills. Most studies have either focused on a specific aspect of nursing or have generally discussed the impact of AI on healthcare without addressing its implications for nursing practice. This review aims to fill this gap and provide a detailed review of the scope of AI in improving nurses’ skills, providing a comprehensive understanding of how AI can be utilized to address the challenges faced by nurses and improve their overall efficiency and effectiveness. Therefore, this scoping review was conducted with the aim of determining the potential of AI in improving nurses’ clinical, educational, decision-making, informational, and research skills.
Information and Methods
This scoping review was conducted based on the framework of Arksey & O'Malley [9] in 2005 and using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews) guidelines [10].
Search strategy
The authors developed search strategies with a medical librarian to identify published reviews on the role of AI in improving nurse education, practice, and clinical skills. Using a combination of keywords, text phrases, Medical Subject Headings (MeSH) and other database-specific terms, strategies were developed to find relevant review articles. Eight electronic databases (PubMed, Scopus, Web of Science, Embase, CINAHL, ProQuest, Microsoft Academic, and OpenGrey) were searched to find all studies (peer-reviewed and grey literature) published up to September 2024. The Google Scholar search engine was used to find relevant references and complete the search coverage. The reference lists of the retrieved articles were also used to find relevant articles. Relevant studies found were screened independently by two investigators.
Keywords used were “Nurse*”, “Nursing Care”, “Nursing Care Plan”, “Nursing Workflows”, “Patient Care”, “Patient Care Planning”, “Patient Monitoring”, “Patient Diagnosis”, “Artificial Intelligence”, “AI”, “Machine Learning”, “Expert Systems”, “Neural Networks”, “Computer”, “Natural Language Processing”, “Fuzzy Logic”, “Data Mining”, “Decision-making”, “Computer-assisted”, “Decision Support Techniques”, “Computer Vision System*”, “Advanced technologies in AI”, “Transfer Learning”, “Support Vector Machine”, “Deep Learning”, “Neural Network*”, “Data Mining” and “Bayesian Network*”.
Selection and inclusion criteria
First, the studies obtained from the electronic and manual search were organized using EndNote software, and then the screening and selection of studies were carried out in two stages based on the inclusion and exclusion criteria. In the first stage, after removing duplicate articles, the titles and abstracts of the articles were reviewed, and in the second stage, the full text of the selected articles was collected and reviewed. At the end of this stage, the reference lists of the remaining articles were reviewed and, if necessary, the responsible authors of the important articles were contacted. Finally, the search and selection process of studies was drawn using the PRISMA flow chart.
Inclusion criteria
- Articles published in Persian and English
- Review articles (review, narrative review, rapid review, scoping review and systematic review)
- Articles related to nursing care
- Journal articles
- Articles that addressed the benefits of AI
Exclusion criteria
- Studies that were not specifically related to the role of AI in improving nurses' skills
- Studies outside the health system
- Studies published in a language other than English and Persian
Quality assessment
The quality assessment of the studies was carried out independently by two authors. Disagreements between the two authors were resolved by a third author through consensus. Finally, if necessary, a consensus table and calculation of the kappa statistic were used.
Data extraction and analysis
For data extraction, a special form was designed in Excel 2013 and the required information, including general information (including study title, year of publication, journal, country, first author) and specific information (including objective, type of study, findings) was extracted. Data collected from included studies on each role that AI could play in nurses' skills were analyzed using narrative methods.
Findings
By searching databases and applying a specific search strategy for each database, 284 studies were identified in the first stage, then by manual searching and reviewing the grey literature, 63 studies were added to this number, which resulted in a total of 347 studies being identified. After removing similar titles, 321 studies were subjected to initial screening. After studying the title and abstract, 175 studies were excluded because they were not relevant to the study objective. Therefore, 146 studies were selected and reviewed. 63 studies met the eligibility criteria for this study, of which 30 were selected and analyzed (Figure 1).
Figure 1. Flow diagram for selection of studies (PRISMA diagram)
30 reviewed studies were conducted from 2020 to 2025 exploring the role of AI in nursing and healthcare. Key objectives include improving education, clinical care, efficiency, and examining AI's challenges and opportunities. The studies highlight the benefits, limitations, and future applications of AI in nursing (Table 1).
Table 1. Characteristics of the included studies
The publication year of the included studies was from 2020 to 2025. Out of the 30 included studies, 15 studies (50%) were published in 2024 (Figure 2).
Figure 2. Year of publication of included studies
Out of the 30 reviewed studies, 14 were scoping reviews (Figure 3).
Figure 3. Study type of included studies
The findings of the studies included in this review according to the categories of themes and sub-themes. The findings of the studies included in this review were categorized into six main themes (education, decision, clinical practice, research, information, and psychiatric nursing; Table 2).
Table 2. Findings of the included studies by themes and sub-themes
Discussion
The purpose of this scoping review was to summarize the findings of review articles published over the past years. The results of our study showed that AI plays a fundamental role in improving the clinical, educational, decision-making, informational, and research skills of nurses. AI (AI) is increasingly becoming a transformative force across various sectors, and nursing is no exception. When considering the potential of AI to enhance clinical, educational, decision-making, information, and research skills among nurses, it’s essential to explore multiple perspectives on this topic.
In education, AI can provide interactive learning experiences through simulations and virtual reality scenarios that mimic real-life situations nurses may face. This hands-on approach helps in honing critical thinking and decision-making skills without putting patients at risk [14]. On the flip side, there may be apprehensions about over-reliance on technology in training environments students might miss out on developing essential interpersonal skills that are crucial for effective nursing practice. Although AI has potential benefits in nursing education, there are still some objections to the development of AI. Writing article and theses is crucial for the success of nursing students and practicing nurses, yet preparing a research paper and thesis is a difficult and daunting task even for experienced writers [16]. Therefore, integrating nursing education, practice, and research with AI has both strengths and limitations. Educational educators and nurses should embrace this technology with a positive view and avoid prohibiting its use. In practice, educators should critically evaluate and use AI appropriately to avoid overreliance. In addition, universities, educational institutions or educational departments should develop and implement guidelines and regulations regarding the proper use of AI in nursing education.
AI serves as a powerful tool for enhancing decision-making processes by providing evidence-based recommendations derived from comprehensive data analysis. For instance, clinical decision support systems (CDSS) can alert nurses about potential medication interactions or deviations from best practices based on current guidelines [12, 21]. One significant advantage of AI in nursing is its ability to enhance clinical decision-making. AI algorithms can analyze vast amounts of patient data quickly, providing nurses with evidence-based recommendations for care [15]. For instance, predictive analytics can help identify patients at risk for complications, allowing nurses to intervene proactively [17, 23]. This not only improves patient outcomes but also empowers nurses by giving them access to advanced tools that augment their clinical judgment [14]. However, there is a risk that such systems could inadvertently contribute to "alert fatigue", where constant notifications lead to desensitization among nursing staff.
From a clinical standpoint, AI can significantly improve patient outcomes through predictive analytics and personalized medicine [29]. For example, AI algorithms can analyze vast amounts of patient data to identify at-risk patients early or suggest tailored treatment plans based on individual health histories. This capability allows nurses to focus more on direct patient care rather than administrative tasks, thereby improving their efficiency and effectiveness [17, 38]. However, there are concerns regarding the reliability of AI systems; if not properly validated, these tools could lead to misdiagnoses or inappropriate treatments.
From a managerial perspective, AI can optimize staffing schedules and resource allocation by analyzing patterns in patient flow and care requirements [15]. This optimization leads to a better work-life balance for nurses and reduced burnout, which is an important issue in the healthcare sector. One of the most compelling arguments for AI in nursing is its ability to automate routine tasks [36]. For instance, AI can assist with scheduling, patient triage, and data entry, allowing nurses to devote more time to direct patient care [12]. By streamlining administrative duties, nurses can focus on critical thinking and clinical judgment skills that are vital for improving patient outcomes [14]. Furthermore, AI algorithms can analyze vast amounts of patient data quickly, providing insights that can inform decision-making processes [17, 20]. This capability not only enhances efficiency but also promotes evidence-based practices. Nonetheless, some argue that excessive reliance on algorithms for decision-making could undermine human judgment and intuition which are vital in complex healthcare settings.
In terms of information management, AI technologies like natural language processing (NLP) can streamline documentation processes by transcribing notes directly into electronic health records (EHRs). This not only saves time but also ensures more accurate records [40]. Conversely, privacy concerns arise with increased digitalization; Safeguarding sensitive patient information becomes paramount as cyber threats evolve.
In the realm of research, AI can streamline data collection and analysis processes. With machine learning algorithms, nurses can conduct more sophisticated analyses of clinical data sets, leading to innovative findings that might have been overlooked through traditional methods [18, 20]. AI also facilitates advanced data analysis techniques that allow for deeper insights into healthcare trends and outcomes over time [41]. By automating literature reviews or synthesizing large datasets quickly, AI empowers nurses engaged in research initiatives to make informed decisions faster than traditional methods would allow [42]. This capability could significantly enhance the quality and quantity of nursing research, ultimately contributing to evidence-based practices that improve patient care on a broader scale. Yet skepticism remains regarding the interpretation of results generated by algorithms researchers must remain vigilant about biases embedded within the data used for training these models.
There is a consensus that AI will not directly replace nurses in the near future. However, it is anticipated that new nurse-patient interactions involving AI will emerge in clinical practice that may enhance nursing performance and the delivery of safe, high-quality care [38]. A review by Ventura-Silva et al. found that the benefits of using AI in nursing care include improved efficiency, decision support and diagnostic accuracy, enhanced interaction and efficient communication, logistical support, reduced workload, and continuous professional development [12]. However, no longitudinal study has been conducted in this area. Therefore, given the lack of useful findings on the effectiveness and efficiency of AI-related tools in real-world scenarios, future research should reflect on the nursing care perspective more than the goals, outcomes, and benefits.
Despite the promising advantages of AI in nursing efficiency, there are notable challenges that must be addressed. One concern is the potential for over-reliance on technology; If nurses become too dependent on AI recommendations without applying their critical thinking skills, it could undermine their professional judgment. In addition, issues of data privacy, legal issues, data security, ethics and resistance to change are of paramount importance as sensitive patient information is managed by these systems [29]. The ethical implications surrounding consent and accountability must also be thoroughly examined as we integrate AI into healthcare practices.
While AI can help with adapting to dynamic situations, critical thinking, patient advocacy, and collaborative efforts, it lacks the emotional intelligence and understanding needed in nursing [29]. Therefore, AI should complement, not replace, healthcare professionals and maintain the essential human element of care. Thus, the unique skills and empathetic aspects of nursing care should be preserved [29, 31].
Limitations
The comprehensiveness of this review led the researchers to select only studies that were fully relevant to the topic based on the title and abstract, so it is possible that a study mentioned nursing in the text and was not included in this review. Another limitation was that we only reviewed articles published in English. Therefore, it is possible that this review missed some relevant articles. There are variations in the use of AI in different settings and countries. This limits generalizability because resources and infrastructure are not the same in different settings and countries. Another limitation was that all relevant articles were published within the last five years, so there is a lack of longitudinal data in this area to assess the lasting impact of AI on nursing care.
This scoping review underscores the significant potential of AI in enhancing various facets of nursing practice. By improving clinical skills, educational methods, decision-making capabilities, and information retrieval, AI stands to revolutionize how nurses operate in today’s healthcare environment. The integration of AI technologies not only fosters better patient outcomes but also equips nurses with the tools necessary for continuous professional development and efficiency. Future research should continue to explore specific applications and address any barriers to implementation to fully realize the benefits AI can bring to the nursing profession.
Conclusion
AI plays a fundamental role in improving the clinical, educational, decision-making, informational, and research skills of nurses.
Acknowledgments: We gratefully acknowledge deputy of research and technology, Yasuj University of Medical Sciences for the support.
Ethical Permissions: This study has been approved by the Research Ethics Committee of the Yasuj University of Medical Sciences (YUMS). The Ethics Code: IR.YUMS.REC.1403.134.
Conflicts of Interests: The authors declare no potential conflict of interest.
Authors' Contribution: Seyed-Nezhad M (First Author), Introduction Writer/Methodologist/Main Researcher/Discussion Writer/Statistical Analyst (28%); Yousefianzadeh O (Second Author), Introduction Writer/Methodologist (17%); Mirzaee MS (Third Author), Discussion Writer/Statistical Analyst (13%); Moradi-Joo M (Fourth Author), Introduction Writer/Methodologist/Main Researcher/Discussion Writer/Statistical Analyst (42%)
Funding/Support: Not applicable.
Keywords:
Artificial Intelligence [MeSH], Nurse [MeSH], Education [MeSH], Decision Making [MeSH], Research [MeSH], Psychiatry [MeSH]
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