Artificial Intelligence (AI) is transforming the healthcare sector through innovative applications such as intelligent monitoring systems, predictive analytics, and diagnostic imaging, which improve clinical decision-making and healthcare efficiency (Topol, 2019). As frontline healthcare professionals, nurses play a crucial role in implementing these technologies in clinical settings. The American Nurses Association (2021) emphasizes the importance of equipping nurses with essential digital competencies to effectively work with AI-based systems.

Previous studies have shown that nurses’ knowledge and awareness significantly influence healthcare practices and patient outcomes. Research conducted among critical care nurses in South Punjab highlighted that adequate knowledge and positive attitudes are essential for improving patient care quality (Mulazim et al., 2025). Similarly, awareness of emerging innovations and novel treatments among nurses is important to ensure that healthcare professionals remain updated with modern medical advancements (Shabir et al., 2025). In addition, studies have reported that nurses’ knowledge is crucial for safe medication administration and reducing clinical errors in healthcare settings (Din et al., 2025).

Workplace challenges such as burnout and occupational health hazards may also affect nurses’ professional performance and their ability to adopt new technologies (Majeed et al., 2025; Ahmad et al., 2025). Despite the growing use of artificial intelligence in healthcare worldwide, its integration into nursing practice remains limited in low-resource regions such as Southern Punjab, Pakistan. Therefore, assessing nurses’ knowledge regarding artificial intelligence is important to identify educational gaps and support the effective adoption of AI technologies in healthcare.

Need for AI Literacy in Nursing

There is growing global recognition of the need for AI literacy among healthcare professionals. Studies have shown that a lack of awareness, limited exposure to digital technologies, and insufficient training contribute to resistance or reluctance in adopting AI (Davenport & Kalakota, 2019; Graziani et al., 2023). In nursing specifically, AI literacy encompasses understanding how AI systems work, evaluating their relevance in clinical scenarios, and navigating ethical challenges related to their use (Frith, 2019; Ng et al., 2022).

In their survey, Abdullah and Fakieh (2020) revealed that healthcare professionals' acceptance of AI is closely tied to their perceived competence and institutional support. In Pakistan’s evolving healthcare landscape, most nurses are not adequately prepared to engage with AI due to limited curricular integration and a lack of professional development opportunities (Ronquillo et al., 2021). This results in gaps in digital readiness that could hinder safe and effective AI adoption.

Barriers to AI Adoption in Low-Resource Settings

Southern Punjab exemplifies many of the systemic barriers seen in low- and middle-income countries (LMICs). These include inadequate internet infrastructure, limited access to modern medical equipment, and insufficient training on digital health tools. Boillat et al. (2021) noted that digital health literacy remains alarmingly low in many LMICs, limiting healthcare professionals' ability to use advanced technologies. Additionally, Elsayed and Sleem (2021) pointed out that nurse managers often express concerns about job displacement, loss of clinical autonomy, and data security.

The perceptions and readiness of nurses must be addressed through targeted educational reforms and institutional support. Educational institutions should incorporate AI concepts into nursing curricula, and healthcare organizations must invest in capacity building to improve digital competencies. Gaughan et al. (2022) emphasized that digital transformation in healthcare is more likely to succeed when staff feel supported and involved in implementation processes.

Tang et al. (2021) explored nurses' views on the use of artificial intelligence (AI) in healthcare and identified several key barriers to its effective adoption. These included limited training opportunities and fears surrounding job displacement. The study highlighted the importance of addressing these concerns through targeted education and institutional backing. Nurses’ comprehension and attitudes toward AI are central to its successful implementation. Therefore, eliminating knowledge gaps and addressing implementation challenges are essential steps toward maximizing AI’s benefits in nursing care and enhancing patient outcomes.

Rationale for the Study

Although studies on artificial intelligence in nursing are growing worldwide, empirical data from Pakistan; especially in impoverished areas like Southern Punjab; still is missing. By evaluating registered nurses' understanding of artificial intelligence, attitudes toward it, and acceptance of it in this area, this study seeks to close that gap. Knowing their current level of readiness will provide insightful information on the structural and educational programs required to facilitate artificial intelligence integration.

Through appropriate AI application, the results of this study can direct strategic changes in nursing education, provide guidance on policy on digital health execution, and improve general patient care quality. Focusing on a geographically and financially disadvantaged region helps this research to add to the larger discussion on fair digital transformation in world health as well.

Significance of Study

The significance of studying how nurses assess AI knowledge stems from its profound ramifications for the future of healthcare delivery, particularly in rapidly evolving technological landscapes like Pakistan. Since nurses are at the forefront of healthcare delivery, their expertise and use of AI technologies are crucial to patient care. First and foremost, this research is significant since it helps close the gap between the theoretical comprehension and practical implementation of AI in healthcare settings. Healthcare organizations can identify areas where educational and training programs should be improved by assessing nurses' proficiency with AI concepts and technologies. This will help ensure that nurses have the skills necessary to employ AI to improve patient outcomes.

Research Questions

1. What is the current level of AI knowledge among nurses in Southern Punjab?
2. How do nurses perceive the role and risks of AI in clinical practice?

Research Objectives

1. To evaluate nurses' understanding of AI concepts and applications relevant to nursing practice.
2. To identify areas of strengths and areas for improvement in nurses'

Literature Review

There are several barriers preventing nurses from learning about AI. The absence of instructional materials and training programs that are geared toward their unique requirements is a significant obstacle (Johnson et al., 2021). Additionally, the quick pace of technological change in AI makes it challenging for nurses to keep up with the most recent developments (James & Brown, 2018). Additionally, it is challenging to incorporate AI education into nursing courses due to institutional limitations, financial constraints, and conflicting priorities (Garcia et al., 2022). The quality and safety of patient treatment might suffer if nurses are not adequately knowledgeable about artificial intelligence. Without adequate training, nurses may struggle to utilize AI technologies efficiently for tasks such as individualized treatment planning, predictive analytics, and decision support (Chen & Wang, 2023). This negates the potential benefits of AI in enhancing patient outcomes (Lee et al., 2021) and raises the risk that AI-generated insights will be misused and misunderstood.

Addressing the gaps in nurses' knowledge of AI necessitates a multifaceted approach. It is imperative to develop unique AI training programs that are specific to the duties and responsibilities of nurses (Robinson et al., 2020). These programs should include both theoretical understanding of AI principles and practical instruction on how to use AI tools in clinical settings. Partnerships between academia, healthcare institutions, and technology companies can help develop thorough AI curricula (Wu et al., 2022). Furthermore, continuous professional development opportunities and support networks may help nurses keep abreast of changes in AI and boost their confidence in utilizing AI technologies (Tan et al., 2023). The integration of AI into healthcare has unparalleled potential for transforming nursing practice and improving patient outcomes. However, in order to realize these benefits, nurses must have a strong command of AI knowledge and skills. In order to ensure that nurses are equipped to utilize the full potential of AI in delivering superior patient care, targeted education and training programs are crucial.

The term "artificial intelligence" (AI) describes a new way of thinking that is similar to that of people. Combining a variety of cutting-edge technologies like natural language processing, machine learning, and computer vision, artificial intelligence is a system. According to Sheikh et al. (2021), artificial intelligence (AI) is a relatively new technology that is sometimes seen as a "black box." The objective is to complete activities that require human intelligence, such as pattern identification, decision-making, and speech recognition. The World Economic Forum defines artificial intelligence (AI) as "act by sensing, interpreting data, learning, reasoning, and recommending." Nevertheless, this categorization is intricate and multifaceted; it addresses a variety of social, ethical, legal, and technological topics, but there is no universally accepted definition.

This study used a descriptive cross-sectional research design to assess nurses’ knowledge, perceptions, and acceptance of artificial intelligence in nursing practice. This study was conducted in different healthcare settings across Southern Punjab, including both public and private hospitals and clinical facilities, to ensure diversity among participants. The target population consisted of registered nurses who were actively working in clinical and hospital environments. A total of 60 nurses participated in the study and were selected through non-probability convenience sampling. This method allowed easy access to participants but may limit the generalizability of the results. Data were collected using a structured close-ended questionnaire adapted from a validated tool developed by Abdullah Abuzaid (2022), ensuring reliability and content validity. The questionnaire included sections on demographic characteristics and questions assessing nurses’ knowledge, perceptions, and acceptance of artificial intelligence. Data collection was completed over one month after obtaining written informed consent from all participants. Participation was voluntary, and confidentiality and anonymity were maintained throughout the study. The overall study duration was from January 1, 2026, to March 1, 2026. Data were analyzed using IBM SPSS Statistics. Descriptive statistics, including frequencies and percentages, were used to summarize the results. Independent variables included age, gender, educational level, years of experience, and work setting, while dependent variables included knowledge, perception, and acceptance of artificial intelligence in nursing. Knowledge scores were categorized as excellent (>80%), good (65–80%), average (50–65%), and poor (<50%).

Demographic Characteristics

A total of 60 registered nurses took part in the study. The sample was predominantly female (80%), with males representing 20%. The largest age group was 30–39 years (36.7%), followed by participants aged 20–29 (30%), 40–49 (20%), 50–59 (10%), and over 60 years (3.3%).

Regarding educational qualifications, 50% of respondents held a Bachelor’s degree in Nursing, 33.3% had a diploma in nursing, and 16.7% possessed a Master’s degree. In terms of work experience, 33.3% reported having 6–10 years of experience, 28.3% had between 11 and 20 years, 25% had 0–5 years, while 13.3% had over 20 years of professional experience. The majority (83.3%) were employed in hospital settings, with the remaining 16.7% working in clinics (Table 01).

Table 01: Demographic Characteristics of Study Participants (N = 60)

Table 4.2: The curriculum should include at least some basic knowledge of AI

 Table 4.2 presents the results about “The curriculum should include at least some basic knowledge of AI”. According to data, majority of respondents are disagreed with the given statement. Mean score 4.16 with 0.984 standard deviation that fall in criterion of acceptance.

Table 4.3: AI should be taught in the undergraduate program.

Table 4.3 presents the results about “AI should be taught in the undergraduate program.”. According to data, majority of respondent are disagreed with the given statement. Mean score 4.19 with 0.797 standard deviation that fall in criterion of acceptance.

Table 4.4: AI should be taught in the postgraduate program.

Table 4.4 shows results that “AI should be taught in the postgraduate program’s”. Results of table shows that 94.0% of respondents are agree and 0.7% are undecided and 5.3% are disagree to the statement. Mean score is 4.24 with standard deviation. Mean score falls in criterion to acceptance the statement.

Table 4.5: I have a basic understanding of AI.

Table 4.5 presents the results about “I have a basic understanding of AI.”. According to data, 100% respondent are disagreed with the given statement. Mean score 4.52 with 0.501 standard deviation that fall in criterion of acceptance.

Table 4.6: I have a working knowledge of AI

Table 4.6 presents the result about “I have a working knowledge of AI.”. According the data, 100% respondent are disagreed. The mean score is 4.43 with 0.496 standard deviation that fall in criterion of acceptance. This explores that Ineffective use of computer in classes.

Table 4.7: I have been trained and educated about AI.

Table 4.7 presents the results about nurses’ view regarding the item “I have been trained and educated about AI.”. According to data, 100% respondent are disagreed. The mean score is 4.46 with 0.500 standard deviation that falls in criterion of acceptance.

Table 4.8: AI plays an important role in nursing.

Table 4.8 presents the result about the nurses’ view regarding the item “AI plays an important role in nursing.”. According to the data, 72.7% respondent are disagreed, 0.7% are undecided, and 6.6% are disagree to the statement. The mean score is 3.55 with .207 standard deviation that falls in criterion of acceptance.

Table 4.9: AI will take place in many nursing applications and practices.

Table 4.9 presents the result about the nurses’ views regarding the item “AI will take place in many nursing applications and practices.”. According the data 100% respondents are disagreed to the statement. The mean value is 4.40 and 0.492 standard deviation that fall in criterion acceptance.

Table 4.10: AI will threaten/disrupt the nursing practice.

Table 4.10 presents the results of nurses views about “AI will threaten/disrupt the nursing practice.”. According to data, 100% respondent are disagreed to the statement. The mean score is 4.45with 0.499 standard deviation that fall in the criterion of acceptance.

Table 4.11: AI will threaten/disrupt the nursing career.

Table 4.11 presents the result of nurses views about “AI will threaten/disrupt the nursing career.”. According to the data 100% respondent are disagreed. The mean score is 4.47 with 0.501 standard deviation that falls in criterion of acceptance.

Table 4.12: AI has no limitation in my work.

Table 4.12 presents the result about the nurses’ view regarding the item “AI has no limitation in my work.”. According to the data, 72.7% respondent are disagreed, 0.7% are undecided, and 6.6% are disagree to the statement. The mean score is 3.55 with .207 standard deviation that falls in criterion of acceptance.

Table 4.13: I use modern audio-visual aids to teach their subjects.

Table 4.13 presents the result of nurses’ view about I use modern audio-visual aids to teach their subjects. According to the data 100% respondent are disagreed. The mean of the data is 4.51 and 0.502 is standard deviation that falls in criterion of acceptance.

Knowledge of Artificial Intelligence

No participants (100%) had received formal AI training or used AI tools clinically, highlighting a critical gap in technological preparedness. However, 14.6% acknowledged AI’s potential utility, suggesting latent openness to adoption.

 Perceptions toward AI in Nursing

Despite their limited exposure to AI, a small proportion (14.6%) believed that AI could have a useful role in nursing practice. However, a substantial majority (85.4%) were either unsure or disagreed with its usefulness, indicating uncertainty or skepticism.

Additionally, 94% of participants disagreed with the idea of integrating AI-related content into nursing undergraduate or postgraduate curricula, while only a few showed interest in such inclusion.

 Acceptance and Readiness for AI

While none of the participants reported using AI in their current clinical settings, the results revealed a degree of openness toward learning, especially if structured training programs were provided. Importantly, none of the respondents believed that AI poses a threat to the nursing profession, which suggests a non-defensive attitude and potential willingness to adapt in the future.

In low-resource areas like Southern Punjab, Pakistan, this study reveals a troubling gap in the integration of artificial intelligence (AI) into nursing education. In other similar settings, infrastructural constraints and outmoded curricula prevent the use of new technology in healthcare environments. Participants' total lack of formal AI training is consistent with this (Boillat et al., 2021; von Gerich et al., 2022). The majority of participants indicated that they were open to receiving future AI training, notwithstanding the high rate of opposition (94%) to integrating AI into nursing education. Reflecting findings from earlier studies that a lack of exposure frequently results in uncertainty or scepticism (Abuzaid, 2022; Castagno & Khalifa, 2020), this tension is probably caused by unfamiliarity rather than active resistance.

Only 14.6% of nurses thought AI was beneficial to their field, which is interesting since it indicates that they have a poor understanding of how AI can enhance clinical decision-making, simplify procedures, and improve patient outcomes. As Buchanan et al. (2020) and Robert (2019) noted, AI is revolutionizing every aspect of nursing practice, including documentation, diagnostics, patient monitoring, and care planning. Nevertheless, nurses are still ill-equipped to actively engage in digital transformation programs unless they have a solid understanding and confidence in these tools. This mirrors similar trends observed worldwide, where frontline healthcare workers frequently lack digital literacy and AI preparedness (Ronquillo et al., 2021; Lambert et al., 2023).

Even though they had reservations, none of the respondents believed that AI posed a danger to the nursing industry. Concerns expressed in Western contexts, where there are frequently fears that AI would replace nursing positions, stand in contrast to this conclusion (Watson et al., 2020). In contrast, the Pakistani nurses in this study seem more open to using AI as long as they receive sufficient assistance. This non-defensive stance offers a chance for focused initiatives aimed at increasing knowledge, demystifying AI, and encouraging nurse-led innovation. According to numerous studies, organized training greatly enhances nurses' acceptance of technology (Schwendimann et al., 2020; Gaughan et al., 2022).

The broader literature emphasizes the importance of integrating AI into both undergraduate nursing education and continuing professional development (American Nurses Association, 2021; Frith, 2019). Doing so would not only prepare nurses for modern clinical environments but also ensure their roles evolve in tandem with technological advancements. AI-powered tools; such as clinical decision support systems, predictive analytics for patient deterioration, and chatbot-assisted triage; are becoming increasingly common in hospitals (Cho et al., 2023; Laukka et al., 2022). Without foundational competence in these systems, nurses risk being sidelined in interdisciplinary care teams or reduced to peripheral roles in data-driven healthcare delivery.

Moreover, qualitative research conducted worldwide (Gao et al., 2020; Ng et al., 2022) demonstrates that nurses frequently cherish humanistic care and worry that AI may dehumanize relationships. Designing AI systems that complement human interaction in nursing, rather than replace it, is necessary to allay such worries. By automating administrative tasks, for instance, AI can free up nurses' time to provide direct patient care, which can increase job satisfaction and lower burnout (Huhtala et al., 2021).

In Pakistan, where digital infrastructure remains underdeveloped in many regions, institutional support is crucial. Leadership buy-in, investment in faculty training, and partnerships with tech developers are essential for building sustainable AI literacy among nurses (Sodeau & Fox, 2022; Sheikh et al., 2023). Policies must also ensure equitable access to training resources so that nurses in rural or resource-constrained areas are not left behind.

In conclusion, this study sheds light on a critical need for systemic reform in nursing education and capacity building. Integrating AI-related content into nursing curricula, supported by institutionally backed learning opportunities and interprofessional collaboration, is essential for creating future-ready nurses. As emphasized by Topol (2019), the goal is not to replace nurses with machines, but to empower them with intelligent tools that elevate care quality. Moving forward, a multi-stakeholder approach involving educators, policymakers, healthcare leaders, and technologists will be vital in translating this vision into reality.

This study exposes a critical AI knowledge deficit among Southern Punjab nurses, rooted in systemic gaps in education and infrastructure. Yet, their non-resistant attitudes suggest readiness for change, provided interventions address contextual barriers (e.g., resource limitations, curricular inertia).

These results highlight a pressing need for comprehensive reforms in nursing education and career advancement. Incorporating AI-focused content into academic curricula, promoting institutionally backed training programs, and fostering interdisciplinary collaboration are critical measures to prepare nurses for the evolving demands of modern healthcare. Strengthening nurses' proficiency in artificial intelligence will not only contribute to better patient care but also reinforce their integral role in technology-driven clinical settings.

Limitations

1. The study utilized a relatively small sample size (n = 60), which may restrict the extent to which the results can be generalized to the broader nursing population.
2. A convenience sampling technique was employed, potentially introducing selection bias and compromising the external validity of the findings.
3. Data collection relied on self-reported measures, which could be influenced by recall errors or social desirability bias, affecting the accuracy of the responses.
4. Since the research was conducted exclusively in Southern Punjab, the findings may not be applicable to other regions within the country or internationally.
5. The study did not employ inferential statistical methods, which limits the ability to assess associations or establish causality between variables.
6. The research design was entirely quantitative, without incorporating qualitative approaches such as interviews or focus group discussions, which could have provided deeper contextual understanding.
7. Although the questionnaire was piloted, it lacked formal psychometric evaluation, such as comprehensive tests of validity and reliability specific to the target population.

Recommendations

Based on the results of this study, the following recommendations are proposed to improve Artificial Intelligence (AI) awareness and integration among nursing professionals in Southern Punjab, Pakistan:

1. Incorporate AI Education into Nursing Curricula:
   Educational institutions should include foundational AI concepts in undergraduate and postgraduate nursing programs. Curriculum content should be contextually adapted and aligned with practical applications in clinical settings.
2. Establish Continuous Professional Development (CPD) Programs:
   Regular training workshops, webinars, and certification courses on AI and digital health should be organized for in-service nurses to enhance their technological competence and readiness.
3. Develop Policy Guidelines for AI Integration:
   Regulatory bodies such as the Pakistan Nursing Council and healthcare institutions should collaborate to create guidelines and frameworks that support safe, ethical, and effective integration of AI into nursing practice.
4. Create Awareness Campaigns within Clinical Settings:
   Hospitals and clinics should run AI awareness drives and internal education programs to familiarize nursing staff with AI’s role in patient care, safety, and decision-making.
5. Invest in Digital Infrastructure and Access:
   Health institutions must ensure nurses have access to AI-enabled tools, reliable internet, and user-friendly interfaces to promote engagement and practical learning.
6. Promote Interdisciplinary Collaboration:
   Encourage partnerships between nurses, IT professionals, educators, and administrators to co-develop AI tools and training material tailored to nursing roles and workflows.
7. Encourage Research on AI in Nursing Practice:
   Support further studies across different regions and institutions to assess broader readiness, barriers, and outcomes related to AI use in clinical care

This study exposes a critical AI knowledge deficit among Southern Punjab nurses, rooted in systemic gaps in education and infrastructure. Yet, their non-resistant attitudes suggest readiness for change, provided interventions address contextual barriers (e.g., resource limitations, curricular inertia).

These results highlight a pressing need for comprehensive reforms in nursing education and career advancement. Incorporating AI-focused content into academic curricula, promoting institutionally backed training programs, and fostering interdisciplinary collaboration are critical measures to prepare nurses for the evolving demands of modern healthcare. Strengthening nurses' proficiency in artificial intelligence will not only contribute to better patient care but also reinforce their integral role in technology-driven clinical settings.

Limitations

1. The study utilized a relatively small sample size (n = 60), which may restrict the extent to which the results can be generalized to the broader nursing population.
2. A convenience sampling technique was employed, potentially introducing selection bias and compromising the external validity of the findings.
3. Data collection relied on self-reported measures, which could be influenced by recall errors or social desirability bias, affecting the accuracy of the responses.
4. Since the research was conducted exclusively in Southern Punjab, the findings may not be applicable to other regions within the country or internationally.
5. The study did not employ inferential statistical methods, which limits the ability to assess associations or establish causality between variables.
6. The research design was entirely quantitative, without incorporating qualitative approaches such as interviews or focus group discussions, which could have provided deeper contextual understanding.
7. Although the questionnaire was piloted, it lacked formal psychometric evaluation, such as comprehensive tests of validity and reliability specific to the target population.

Recommendations

Based on the results of this study, the following recommendations are proposed to improve Artificial Intelligence (AI) awareness and integration among nursing professionals in Southern Punjab, Pakistan:

1. Incorporate AI Education into Nursing Curricula:
   Educational institutions should include foundational AI concepts in undergraduate and postgraduate nursing programs. Curriculum content should be contextually adapted and aligned with practical applications in clinical settings.
2. Establish Continuous Professional Development (CPD) Programs:
   Regular training workshops, webinars, and certification courses on AI and digital health should be organized for in-service nurses to enhance their technological competence and readiness.
3. Develop Policy Guidelines for AI Integration:
   Regulatory bodies such as the Pakistan Nursing Council and healthcare institutions should collaborate to create guidelines and frameworks that support safe, ethical, and effective integration of AI into nursing practice.
4. Create Awareness Campaigns within Clinical Settings:
   Hospitals and clinics should run AI awareness drives and internal education programs to familiarize nursing staff with AI’s role in patient care, safety, and decision-making.
5. Invest in Digital Infrastructure and Access:
   Health institutions must ensure nurses have access to AI-enabled tools, reliable internet, and user-friendly interfaces to promote engagement and practical learning.
6. Promote Interdisciplinary Collaboration:
   Encourage partnerships between nurses, IT professionals, educators, and administrators to co-develop AI tools and training material tailored to nursing roles and workflows.
7. Encourage Research on AI in Nursing Practice:
   Support further studies across different regions and institutions to assess broader readiness, barriers, and outcomes related to AI use in clinical care.

1. Abdullah, R., & Fakieh, B. (2020). Health care employees’ perceptions of the use of artificial intelligence applications: Survey study. Journal of Medical Internet Research, 22(5). https://doi.org/10.2196/17620
2. American Nurses Association. (2021). Nursing: Scope and standards of practice (4th ed.).
3. Ahmad A, Arshad H, Saman M, Naveed M, Shafiq N, Hussain MB, Danish M. OCCUPATIONAL HEALTH HAZARDS AMONG NURSES: ASSESSING KNOWLEDGE, ATTITUDES, AND PRACTICES IN SOUTHERN PUNJAB, PAKISTAN.
4. Abuzaid, M.M., 2022. Knowledge and attitudes of healthcare professionals towards artificial intelligence: A cross-sectional survey. [Journal/Publisher details if available].
5. Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2020). Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review. JMIR Nursing, 3(1). https://doi.org/10.2196/23939
6. Boillat, T., et al. (2021). Digital health literacy and readiness to adopt health IT among healthcare professionals. International Journal of Medical Informatics, 148, 104394. https://doi.org/10.1016/j.ijmedinf.2021.104394
7. Castagno, S., & Khalifa, M. (2020). Perceptions of Artificial Intelligence Among Healthcare Staff: A Qualitative Survey Study. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/FRAI.2020.578983
8. Cho, I., Cho, J., Hong, J. H., Choe, W. S., & Shin, H. K. (2023). Utilizing standardized nursing terminologies in implementing an AI-powered fall-prevention tool to improve patient outcomes: a multihospital study. Journal of the American Medical Informatics Association : JAMIA, 30(11), 1826–1836. https://doi.org/10.1093/JAMIA/OCAD145
9. Din, S.S., Bhullar, M., Fatima, A., Fatima, A. and Danish, M., 2025. Nurses’ Knowledge and Practices Regarding Safe Medication Administration at The Children’s Hospital, Lahore. Journal of Health, Wellness and Community Research, pp.e995-e995.
10. Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94
11. Elsayed, W., & Sleem, W. (2021). Nurse Managers’ Perspectives and Attitude Toward Using Artificial Intelligence Technology in Nursing Settings. Assiut Scientific Nursing Journal, 0(0), 0–0. https://doi.org/10.21608/ASNJ.2021.72740.1159
12. Etikan, I., Musa, S.A. and Alkassim, R.S., 2016. Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), pp.1–4.
13. Frith, K. H. (2019). Artificial Intelligence: What Does It Mean for Nursing? Nursing Education Perspectives, 40(4), 261. https://doi.org/10.1097/01.NEP.0000000000000543
14. Gao, S., He, L., Chen, Y., Li, D., & Lai, K. (2020). Public perception of artificial intelligence in medical care: Content analysis of social media. Journal of Medical Internet Research, 22(7). https://doi.org/10.2196/16649
15. Gaughan, M. R., Kwon, M., Park, E., & Jungquist, C. (2022). Nurses’ Experience and Perception of Technology Use in Practice: A Qualitative Study Using an Extended Technology Acceptance Model. CIN - Computers Informatics Nursing, 40(7), 478–486. https://doi.org/10.1097/CIN.0000000000000850
16. Graziani, M., Dutkiewicz, L., Calvaresi, D., Amorim, J. P., Yordanova, K., Vered, M., Nair, R., Abreu, P. H., Blanke, T., Pulignano, V., Prior, J. O., Lauwaert, L., Reijers, W., Depeursinge, A., Andrearczyk, V., & Müller, H. (2023). A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences. Artificial Intelligence Review, 56(4), 3473–3504. https://doi.org/10.1007/S10462-022-10256-8
17. Huhtala, M., Geurts, S., Mauno, S., & Feldt, T. (2021). Intensified job demands in healthcare and their consequences for employee well-being and patient satisfaction: A multilevel approach. Journal of Advanced Nursing, 77(9), 3718–3732. https://doi.org/10.1111/JAN.14861
18. Lambert, S. I., Madi, M., Sopka, S., Lenes, A., Stange, H., Buszello, C. P., & Stephan, A. (2023). An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. Npj Digital Medicine, 6(1). https://doi.org/10.1038/S41746-023-00852-5
19. Laukka, E., Hammarén, M., & Kanste, O. (2022). Nurse leaders’ and digital service developers’ perceptions of the future role of artificial intelligence in specialized medical care: An interview study. Journal of Nursing Management, 30(8), 3838–3846. https://doi.org/10.1111/JONM.13769
20. Mulazim S, Bilal M, Khan N, Danish M, Jamal F. Evaluation of Knowledge and Attitudes Toward Palliative Care Among Critical Care Nurses in Tertiary Hospitals of South Punjab, Pakistan. Journal of Health, Wellness and Community Research. 2025 Nov 13:e943-.
21. Majeed R, Fatima A, Andleeb H, Fatima A, Danish M, Ahmad Z, Jamal F. Assessment of Burnout and Job Satisfaction among Nurses Working in High-Stress Units of Tertiary Hospitals of Bahawalpur. Indus Journal of Bioscience Research. 2025 Apr 30;3(4):669-76.
22. Robert, N. (2019). How artificial intelligence is changing nursing. Nursing Management, 50(9), 30–39. https://doi.org/10.1097/01.NUMA.0000578988.56622.21
23. Ronquillo, C. E., Peltonen, L. M., Pruinelli, L., Chu, C. H., Bakken, S., Beduschi, A., Cato, K., Hardiker, N., Junger, A., Michalowski, M., Nyrup, R., Rahimi, S., Reed, D. N., Salakoski, T., Salanterä, S., Walton, N., Weber, P., Wiegand, T., & Topaz, M. (2021). Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative. Journal of Advanced Nursing, 77(9), 3707–3717. https://doi.org/10.1111/JAN.14855
24. Setia, M.S., 2016. Methodology series module 3: Cross-sectional studies. Indian Journal of Dermatology, 61(3), p.261.
25. Sheikh, H., Prins, C., & Schrijvers, E. (2023). Artificial Intelligence: Definition and Background. 15–41. https://doi.org/10.1007/978-3-031-21448-6_2
26. Sodeau, A., & Fox, A. (2022). Influence of nurses in the implementation of artificial intelligence in health care: A scoping review. Australian Health Review. https://doi.org/10.1071/AH22164
27. Shabir K, Ramzan I, Najeeb H, Danish M, Haider R, Fatima A, Hussain MB, Jamal F. ASSESSING EMERGENCY ROOM NURSES’AWARENESS OF NOVEL TREATMENTS AND EMERGING INNOVATIONS IN ANAPHYLAXIS MANAGEMENT.
28. Schwendimann, R., et al. (2020). Assessment of nurses’ competence in using digital health tools: A review. Journal of Nursing Scholarship, 52(3), 281–290. https://doi.org/10.1111/jnu.12544
29. Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
30. von Gerich, H., Moen, H., Block, L. J., Chu, C. H., DeForest, H., Hobensack, M., Michalowski, M., Mitchell, J., Nibber, R., Olalia, M. A., Pruinelli, L., Ronquillo, C. E., Topaz, M., & Peltonen, L. M. (2022). Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence. International Journal of Nursing Studies, 127. https://doi.org/10.1016/J.IJNURSTU.2021.104153
31. Watson, D., Womack, J., & Papadakos, S. (2020). Rise of the Robots: Is Artificial Intelligence a Friend or Foe to Nursing Practice? Critical Care Nursing Quarterly, 43(3), 303–311. https://doi.org/10.1097/CNQ.0000000000000315