As the healthcare industry continues to evolve, the integration of Artificial Intelligence has become a topic of increasing interest and significance, particularly in the realm of nursing practice.

Understanding the perspectives, knowledge base, and attitudes of nursing professionals toward AI is crucial for ensuring a smooth and effective transition into a future where AI plays a more prominent role [1]. The rapid development of AI technologies has led to their application in various aspects of healthcare, ranging from diagnostics and treatment to patient monitoring and administrative tasks.

This expansion necessitates a thorough examination of how these technologies impact nursing science and healthcare systems, with a focus on enhancing the quality, efficiency, and accessibility of care [2]. The integration of AI in nursing has the potential to transform clinical benefits for patients through more advanced, accurate, practical, effective, efficient, economical, and personalized care [2].

However, the introduction of AI into nursing practice is not without its challenges and potential drawbacks. These include concerns about data privacy, ethical considerations, algorithmic biases, and the potential for unintended consequences that could negatively impact the nursing profession and the fundamental principles of nursing care [2].  It is therefore essential to explore and discuss the multifaceted impact of AI on nursing science and healthcare to ensure that its implementation aligns with the goals of providing optimal patient care and supporting the nursing workforce [2].

The rapid evolution of healthcare necessitates the exploration of artificial intelligence as a transformative technology, making it crucial to understand the perspectives of nursing professionals regarding its integration into future nursing care [1].

Artificial intelligence is rapidly emerging as a significant force, poised to revolutionize various sectors, and healthcare stands out as a domain ripe for its transformative potential [1,3]. The integration of AI into healthcare is not merely a technological advancement; it represents a paradigm shift that demands careful consideration of its

Implications for healthcare professionals, especially nurses who constitute the largest segment of the healthcare workforce [4,3].

The advent of AI in healthcare presents both opportunities and challenges, requiring nurses to adapt and acquire new skills to effectively collaborate with AI systems. It is imperative to investigate the level of knowledge and attitudes among nurses concerning AI in clinical practice, as their acceptance and adoption of these technologies will significantly influence the successful implementation of AI-driven healthcare solutions [4].

The integration of AI holds the promise of improving medical records, expanding access to quality medical care, and enhancing the overall quality of services delivered to patients [2]. AI systems are being developed to aid in the timely identification of patients experiencing declining conditions, facilitate the distribution of workloads, and expedite care coordination processes [4]. Nurses who utilize AI technologies may experience gains in clinical expertise, improvements in patient well-being, and increased employee efficiency [4]. To harness the full potential of AI in healthcare, it is essential to foster interdisciplinary collaboration, establish ethical guidelines, and safeguard patient rights [2]. The integration of AI into nursing practice has the potential to transform healthcare delivery and improve patient outcomes, necessitating that nurses and healthcare institutions embrace AI and prepare for the future. Nurses must engage in continuous learning to stay informed about AI advancements, acquire fundamental AI concepts and skills, and actively seek training programs and resources for up skilling [4].

While AI offers numerous advantages, it is crucial to acknowledge and address the potential negative impacts and unintended consequences that may arise from its application in nursing and the broader healthcare system [2].

The current challenge lies in translating this expanded technology into tangible clinical benefits for patients, through more advanced, accurate, practical, effective, efficient, economical, and personalized care [2].

The integration of artificial intelligence into healthcare is rapidly transforming the landscape of medical practice, necessitating a comprehensive understanding of its implications for nursing professionals [1]. The advent of AI in healthcare presents a paradigm shift, with applications ranging from personalized patient care and enhanced diagnostic accuracy to predictive analytics and the streamlining of telemedicine services.  As AI technologies become increasingly prevalent, it is imperative to assess the knowledge, attitudes, and perceptions of nurses regarding their integration into clinical practice. The exploration of nurses' viewpoints is particularly crucial, considering their pivotal role in direct patient care and their potential to serve as intermediaries between advanced technologies and patient well-being [1]. It is important to address the knowledge gaps that may exist within the nursing discipline regarding AI and to promote educational initiatives that equip nurses with the necessary skills and understanding to effectively utilize AI-driven tools. Nursing practice is poised to be significantly impacted by the incorporation of AI, thus necessitating further research into nurses' understanding and perspectives on this technological integration. The primary objective is to evaluate the existing level of knowledge, attitudes, willingness, and organizational readiness among nurses concerning the assimilation of AI into their daily routines. By examining these factors, healthcare organizations can proactively address any potential barriers and foster a supportive environment for the successful adoption of AI technologies in nursing practice.

The transformative potential of AI in healthcare extends to various facets of nursing, including expanding access to quality medical care, enhancing the accuracy and efficiency of medical record management, and elevating the overall quality of healthcare services [2]. Moreover, AI algorithms can analyze vast datasets to identify patterns and predict potential health risks, enabling nurses to implement targeted interventions and preventive measures.

A study to assess the knowledge and attitude towards artificial intelligence among nurses of pediatric private ward

Objectives of the Study

General Objective

This study aims to assess knowledge and attitudes toward AI-integrated tools used in clinical practice of nursing personnel

Specific Objective

• To assess the current knowledge of AI Robert among nurses in clinical practice.
• To analyze the attitude of nurses towards uses of AI Robert in clinical practice

Research Questions

• What is the current level of knowledge among nurses regarding the applications of AI in clinical practice?
• What are the general attitudes of nurses towards the integration of AI into clinical workflows?

Hypotheses

H1: There is no association between nurses’ educational level and their knowledge of AI in clinical practice.

Operational Definition

Knowledge

Knowledge refers to the understanding and awareness of facts, information, descriptions, or skills acquired through experience or education. It can be implicit or explicit, ranging from practical skills to theoretical understanding. In the context of your study, nurses' knowledge about AI would encompass their understanding of:

• What AI is: Basic concepts, definitions, and different types of AI.
• How AI is used in healthcare: Specific applications, such as diagnosis, treatment planning, and patient monitoring.
• The potential benefits and risks of AI in healthcare: Improved accuracy, efficiency, and patient outcomes versus ethical concerns, bias, and job displacement.
• How to interact with AI systems: Practical skills in using AI tools in clinical practice.

Attitude

Attitude represents a settled way of thinking or feeling about something, typically reflected in a person's behavior. It is a learned predisposition to respond in a consistently favorable or unfavorable manner with respect to a given object.

Artificial Intelligence: A term coined by emeritus Stanford Professor John McCarthy in 1955, was defined by him as "the science and engineering of making intelligent machines". Much research has humans program machines to behave in a clever way, like playing chess, but, today, we emphasize machines that can learn, at least somewhat like human beings do.         

Nurses: In study nurses is refer to the selected group of nursing professionals who participate in a study.

Assumptions

• Nurses will have different levels of understanding and familiarity with AI, ranging from basic awareness to in-depth technical knowledge.
• it’s assumed that participants will provide honest and accurate responses to the study questions.
• Nurses are likely to hold diverse attitudes towards AI, influenced by factors like age, experience, education, and exposure to AI technologies.

Limitation

• This study will be conducted in a very sample size.
• This study may not be generalizes.
• The dynamic advancements in AI necessitate continuous updates to assessment instruments to accurately reflect the current state of the field.
• Furthermore, the rapidly evolving nature of AI technologies implies that any assessment of knowledge and attitudes captures a snapshot in time.

Projected Outcome

• It is anticipated that the study will reveal a spectrum of knowledge levels among nurses, influenced by factors such as their educational background, years of experience, specialization, and ongoing professional development activities.
• Furthermore, the study is expected to highlight the areas where nurses demonstrate strong knowledge and competence, as well as identify gaps or areas where further education and training may be beneficial
• A positive attitude is generally associated with improved understanding of AI system.

Introduction

Artificial intelligence is rapidly transforming healthcare, offering the potential to revolutionize clinical practice. AI in healthcare refers to the use of complex algorithms and software to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. AI systems are designed to learn from vast amounts of data, identify patterns, and make predictions or recommendations to assist healthcare professionals in clinical decision-making.

Understanding Artificial Intelligence in Healthcare

The healthcare sector, known for its intricate dynamics and multifaceted challenges, has increasingly become a focal point for the integration of artificial intelligence.

The integration of artificial intelligence into healthcare represents a significant paradigm shift, holding the potential to reshape clinical practice and enhance patient outcomes. This integration holds the promise of not only enhancing patient care but also improving the overall quality of life for individuals seeking medical assistance. The ongoing advancements in AI technologies have set the stage for a potential revolution in healthcare, paving the way for AI's seamless incorporation into clinical practice, leading to improvements in disease diagnosis, treatment recommendations, and patient engage. The rise of AI in healthcare dates back to the early days of computer science, when researchers first conceived of machines capable of mimicking human intelligence.

The rise of AI in healthcare dates back to the early days of computer science, when researchers first conceived of machines capable of mimicking human intelligence.

The transformative potential of AI in healthcare is vast, encompassing personalized care, improved diagnostic accuracy, predictive analytics, and the expansion of telemedicine. AI's capacity to analyze large datasets facilitates the identification of patterns and anomalies, leading to more accurate and timely diagnoses.

Nurses Knowledge about Artificial Intelligence

The burgeoning field of artificial intelligence is rapidly permeating various sectors, and healthcare is no exception, necessitating a thorough examination of nurses' understanding and perspectives on its integration into clinical practice. Nursing, as a discipline rooted in both science and compassionate care, finds itself at a pivotal juncture where technological advancements, particularly in AI, are poised to reshape the delivery of healthcare services.

As the healthcare landscape evolves, marked by an increasing demand for efficiency, accuracy, and personalized care, AI technologies offer innovative solutions that can augment nurses' capabilities and improve patient outcomes.

However, the successful adoption and implementation of AI in nursing hinges on the readiness and acceptance of nursing professionals, which in turn is influenced by their knowledge, attitudes, and perceptions of AI [1]. The increasing complexity of healthcare demands and the continuous evolution of technology have led to the introduction of AI and AI-powered robots in nursing care. As AI technology advances, becomes more efficient, and cost-effective, the opportunities and pressures to incorporate it into nursing care will undoubtedly increase.

The integration of AI in nursing presents both opportunities and challenges, demanding a nuanced understanding of its potential impact on the profession. The rapid development of AI and its expanding applications across the healthcare sector necessitate a proactive approach to ensure that nurses are well-prepared for this technological shift.

Impact of AI on Nurses Practice

The integration of artificial intelligence into the healthcare sector is rapidly transforming the landscape of nursing practice, necessitating a comprehensive understanding of its multifaceted implications. Nursing professionals' perceptions regarding the role of AI in shaping the future of healthcare are crucial in this context [1].

AI's transformative potential spans personalized care, diagnostic accuracy, predictive analytics, and telemedicine, yet its integration presents complexities such as data privacy, ethical considerations, and algorithmic biases. As AI continues to evolve, it is imperative to evaluate its impact on nursing science and healthcare systems to ensure the delivery of approximate nursing care that is advanced, accurate, practical, effective, efficient, economical, and personalized [2].

This necessitates a critical assessment of AI's transformative potential to equip researchers with a deeper understanding of its current and future impact on healthcare.

The ongoing evolution of the healthcare landscape is marked by the emergence of artificial intelligence as a transformative force [1].

The integration of AI into nursing science and healthcare settings is essential to meet the escalating demands for nursing care, although unintended consequences could negatively impact the nursing profession and the core purpose of nursing practice [2]. This integration represents a paradigm shift in how medical care is delivered and managed within hospitals and clinics.

Barriers and Challenges in AI Adoption

The integration of Artificial Intelligence into various sectors presents a transformative opportunity, yet its widespread adoption is often hindered by a complex interplay of barriers and challenges. These obstacles manifest across organizational, technical, and individual dimensions, necessitating a comprehensive understanding to facilitate smoother AI implementation [5].

One significant impediment lies in the gap between technological advancements and their practical application within specific contexts, particularly in complex systems like healthcare [6].

The enthusiasm of decision-makers and technology proponents to leverage AI for enhancing healthcare delivery often clashes with the on-the-ground realities, where initiating the necessary changes to fully realize the benefits of AI while mitigating potential negative impacts proves challenging [7].

The effective implementation of AI systems necessitates a comprehensive, multi-dimensional approach that transcends purely technological capabilities, emphasizing meticulous consideration of ethical implications, the establishment of robust data governance frameworks to ensure data privacy and security, and the integration of human-in-the-loop mechanisms to maintain accountability and oversight [8-13].

A critical gap exists in the literature regarding solutions to promote the adoption of AI technologies across complex and often siloed systems. While technical breakthroughs in AI are widely shared, less attention is paid to overcoming the practical obstacles that impede their integration. If healthcare professionals are reluctant to embrace AI, or if concerns regarding data bias and legal liability remain unaddressed, the potential of AI breakthroughs will remain theoretical [6].

Conclusion

In conclusion, the integration of artificial intelligence into healthcare is rapidly transforming the landscape of medical practice, necessitating a comprehensive understanding of the knowledge, attitudes, and perceptions of nursing professionals regarding this technological revolution As AI continues to penetrate various facets of healthcare, from personalized care and diagnostic accuracy to predictive analytics and telemedicine, it is crucial to acknowledge both its transformative potential and inherent complexities.

The Technology Acceptance Model (TAM) was proposed by Fred D. Davis in 1986 as an extension of the Theory of Reasoned Action (TRA). It is one of the most widely used theoretical frameworks to study user acceptance of technology in healthcare, and education.

TAM focuses on two primary determinants of acceptance: perceived usefulness (the degree to which a person believes a system will be beneficial for their work) and perceived ease of use (the degree to which a person believes a system will be effortless to use). These factors influence a user's attitude toward the technology, which in turn shapes their behavioural intention and actual use of the system.

TAM explains how users come to accept and use a technology. It emphasizes two key beliefs that influence a person’s attitude and intention to use a new system:

Core Constructs of TAM

• Perceived Usefulness (PU):

Definition: The degree to which a person believes that using a particular system will enhance their job performance.

• Perceived Ease of Use (PEOU): The degree to which a person believes that using a particular system will be free of effort.
• Attitude Toward Using (ATU): The user's positive or negative feeling about performing a specific behavior (using the system).
• Behavioural Intention to Use (BI): The measure of the user's subjective probability that they will use a specific system.

Introduction: This section details the methodology for assessing the knowledge and attitudes of nurses regarding artificial intelligence in clinical practice. A mixed-methods approach will provide a comprehensive understanding of this complex topic, combining quantitative surveys for breadth of data and qualitative interviews for depth of understanding.

Research Approach: This study adopted a quantitative research approach to allow the collection of numerical data that can be statistically analyzed to identify patterns, trends and relationship by using standardized tool, the study quantified knowledge level and attitude the made finding easier to generalized and compare.

Research Design: This study adopted a descriptive cross sectional research design that explores the knowledge and attitude of staff nurses towards Artificial intelligence in clinical practice.

Setting of the Study: This study was conducted in Pediatric Private Ward, Christian Medical College Hospital, Vellore. 

Population: The populations compromised of all the staff nurses working in pediatric private ward.

Participants: The participants in the study comprised of all the nurses working in pediatric private ward who meets the inclusion criteria.

Sample Size: Total sample size for the project study was limited to 21 participants.

Sampling Technique: Purposive sampling technique was adopted to select the subject for the study .

Criteria for Sample Selection

Inclusion Criteria

• Registered nurses working in pediatric private ward.
• Willing to give informed consent, and participate in the study.
• Able to read and study the language used in the study.

Exclusion Criterion

• Non nursing staff.
• Nurses who decline consent or are unwilling to participate.

Description of the Instrument

It consists of three sections

Section A- consist of socio-demographic variables such as age, gender, marital status, highest level of educational, years of experience, nature of work, type of unit.

Section B- It consists of seven items measured as “Yes or No” questions to the common terms of AI used in healthcare.

Section C- It is a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). It measures the nurses’ attitudes toward the application of AI in healthcare settings. Consisting 19 items out of which 7 items are negative and 12 items are positive

Score Interpretation

Strongly disagree-1

Disagree- 2

Neutral – 3

Agree – 4

Strongly agree -5

If for negatively worded items score is reversed

Validity and Reliability

Reliability was established by using internal consistency, with Cronbach alpha.

Reliability coefficient for knowledge towards AI was found to be 0.795, as reported by Swed et al and reliability coefficient for attitude towards AI was found to be 0.88 for the Positive GAAIS (12 items) and 0.82 for the Negative GAAIS (7 items), as reported by Schepman and Rodway.

Data Collection Procedure

The data was collected for the period of 1 week, from Monday to Saturday between 11.30am to 12.30pm.

Women who met the inclusion criteria will be selected using simple random sampling technique.

A good rapport was built with staff nurses.

Information sheet was provided to the staff nurses in the language that she will be able to comprehend. Written consent was obtained from her after explaining the purpose of the study to them.

Data was collected through self-administered questions.

The investigator checked for any missing response on completion.

Ethical Consideration

The study was conducted after an approval from the Head of Department. A written informed consent was obtained from the Subjects. Data obtained from the subject was kept confidential in a password protected file.

The Statistical Analysis

Data was analyzed using statistical package for social science (SPSS) version 21 and results presented in forms of tables, pie chart and bar graph.

Data analysis is the process of inspecting, cleaning, transforming and modeling data to the extract useful information, make conclusions, support decision making and applied in various field. It is crucial for drawing meaningful insight from research data.

The Data Analysis Tool

The data collected was analyzed using Microsoft Excel and SSPS (statistical package for social science) version 21 to generate frequencies and percentage the finding were presented in the form of tables, pie charts and graphs as seen below.

Distribution PF the Respondents by Demographic Characteristics (N=21)

Table 1: Distribution of the respondents according to age.

Table 1 shows that majority 8(38.0) respondents are between (26-35) years of age, while the minority 2(9.5) respondents are between (46-55) years of age.

Table 2: Distribution of the respondents according to the gender.

Table 2 shows that majority 21(100.0) respondents are female.

Table 3: Distribution of respondents according to the marital status.

Table 3 shows that majority 15(71.5) respondent are married, while the minority 6(28.5) respondents are single.

Table 4: Distribution of respondents according to their highest level of education.

Table 4 shows that majority 17(80.9) respondent are holding diploma in nursing while only 4(19.1) are having baccalaureate.

Table 5: Distribution of the respondents according to their years of experience.

Table 5 shows that majority 10(47.7) respondents are having ≥10 years of experience, while the minority 1(4.7) are having <1 years of experience.

Table 6: Distribution of the respondents according to their nature of work.

Table 6 shows that majority 15(71.4) respondents are worked full time, while the minority 2(9.5) respondents were did night shift

Table 7: Distribution of the respondents according to the type of unit.

Table 7 shows that majority 21(100.0) respondents are working in gynecology unit.

Distributions of the Respondents According to Knowledge

Table 8: Distribution of the respondents according to the knowledge.

Table 8 shows that present the frequencies and percentages of nurses’ responses to items assessing their awareness regarding AI technology. For the definition of AI, 90.5% of nurses indicated having basic information, while 9.5% reported not having such knowledge. A substantial majority (90.5%) acknowledged being aware of the use of AI in the medical field, with 9.5% indicating no familiarity with AI applications. In terms of educational background, 66.6% of nurses reported that the basics of AI were covered in their undergraduate education, while the minority (33.4%) stated otherwise. Additionally, 57.2% of nurses reported encountering and using AI means in the medical field, whereas 42.8% had not. Concerning AI-integrated applications, 52.3% of nurses reported knowledge about it, while 47.6% indicated a lack of knowledge. Furthermore, 33.3% of nurses reported encountering AI in nursing diagnosis through electronic care systems, and 23.8% reported that they had used AI to calculate drug doses.

Distribution of the Respondents According to the Attitude (N=21)

Figure 1: There are many beneficial applications of AI.

This graph shows that majority 71% of respondents are agree, while only 5% of respondents are neutral to beneficial application of artificial intelligence.

Figure 2: I am impressed by what AI can do.

This chart shows that majority 86%of respondent agree while minority 14.7 percentages are impressed by what AI can do.

Figure 3: AI is exciting.

This chart shows that majority 52%of respondents agree while minority 19% of respondents is neutral to AI is exciting

Figure 4: AI can have positive impacts on people's well-being.

This chart shows that majority 57%of respondents agree while minority 5% of respondents is strongly agree to AI can have positive impact on people’s wellbeing.

Figure 5: Artificial intelligence Robert can help people feel happier.

This chart shows that majority 48%of respondents neutral while minority 5% of respondents strongly agree to AI can help people feel happier.

Figure 6: I am interested in using artificially intelligent systems in my daily life.

This chart shows that majority 67%of respondents agree while minority 5% of respondents strongly agree to, I am interested in using AI system in my daily life.

Figure 7: AI can provide new economic opportunities for this country.

This chart shows that majority 57%of respondents agree while minority 43% of respondents neutral to AI can provide new economic opportunities for this country.

Figure 8: Much of society will benefit from a future full of AI.

This chart shows that majority 57%of respondents agree while minority 43% of respondents neutral to AI can provide new economic opportunities for this country.

Figure 9: Artificial intelligence might take control of people.

This chart shows that majority 52%of respondents neutral while minority 24% of respondents are both disagree and agree to AI might take control of people.

Figure 10: People like me will suffer if AI is used more and more.

This chart shows that majority 62%of respondents neutral while minority 5% of respondents is strongly agree to people like me will suffer if AI is used more and more.

Figure 11: I think artificially intelligent systems make many errors.

This chart shows that majority 57%of respondents neutral while minority 19% of respondents is agree to I think artificially intelligent systems make many errors.

Figure 12: I am scared when I think about future uses of artificial intelligence.

This chart shows that majority 62%of respondents agree while minority 9% of respondents is disagree to, I am scared when I think about future uses of artificial intelligence.

Figure 13: I think AI is dangerous.

This chart shows that majority 48% of respondents disagree while minority 5% of respondents is agree to, I think AI is dangerous.

Figure 14: I would like to use Artificial intelligence Robert in my job.

This chart shows that majority 52%of respondents neutral while minority 48% of respondents is agree to, I would like to use Artificial intelligence Robert in my job.

Figure 15: For routine transactions, I would rather interact with an AI system than with a human.

This chart shows that majority 43%of respondents neutral while minority 5% of respondents is strongly agree to for routine transactions; I would rather interact with an AI system than with a human.

Figure 16: An Artificial intelligence Robert would be better than an employee in many routine jobs.

This chart shows that majority 43%of respondents agree while minority 24% of respondents is disagree to an Artificial intelligence Robert would be better than an employee in many routine jobs.

Figure 17: Artificial intelligence Robert can perform better than humans.

This chart shows that majority 39%of respondents agree while minority 33% of respondents is neutral to artificial intelligence Robert can perform better than humans.

Figure 18: I find AI scary.

This chart shows that majority 43%of respondents neutral while minority 28% of respondents is agree to I find AI is scary.

Figure 19: Sometimes Organizations misuse Artificial intelligence.

This chart shows that majority 62%of respondents neutral while minority 14% of respondents is disagree to sometimes organizations misuse Artificial intelligence.

Table 9: Over all attitude (N=21).

This table shows that most 14(2.61- 3.40) of respondents had neutral attitude while only 7(4.21- 5.00) of the participants has positive attitude.

Nurses Knowledge Regarding Artificial Intelligence in Clinical Practice 

The findings of this study indicate that while a significant majority (90.5%) of nurses reported having basic knowledge of AI, a notable percentage (9.5%) lacked awareness. This aligns with previous literature suggesting that AI in healthcare is still an emerging field with variable levels of adoption among healthcare professionals.

Despite awareness of AI’s presence in medical settings, more than half of the nurses (52.3%) had knowledge about AI-integrated applications, while 47.6% indicated a lack of familiarity. This suggests a gap in practical exposure and formal training on AI usage in nursing practice. Furthermore, 33.3% of nurses reported encountering AI in nursing diagnosis, while only 23.8% had used AI for drug dose calculations. This highlights that AI is being integrated into certain clinical applications, but its utilization remains limited.

Nurses Attitude Regarding Artificial Intelligence in Clinical Practice

In terms of attitude, the majority of respondents (14 out of 21) demonstrated a neutral stance toward AI (mean score range: 2.61–3.40), suggesting hesitancy or uncertainty regarding AI’s role in clinical practice. Meanwhile, 7 participants had a positive attitude (mean score range: 3.41–4.20), reflecting a willingness to engage with AI-based systems. Notably, no participants exhibited negative or very negative attitudes, which may suggest openness to AI despite limited knowledge and experience.

The results suggest that while AI is recognized as an important tool in healthcare, gaps exist in practical knowledge and application, which may be influencing the neutral attitude among nurses. Increased education and hands-on experience with AI-integrated systems could potentially improve both knowledge and attitudes.

Nursing Implication

• Education & Training: Nursing curricula should incorporate AI-related modules to enhance knowledge and competency. Continuing education programs should also include AI applications in clinical decision-making.
• Policy & Guidelines: Healthcare institutions should develop guidelines for AI use in nursing, ensuring ethical considerations and patient safety.
• Clinical Integration: AI-based tools should be gradually introduced into nursing workflows, with hands-on training sessions to improve adoption rates.
• Interdisciplinary Collaboration: Nurses should collaborate with AI developers and IT professionals to ensure that AI systems meet the practical needs of patient care.
• Ethical & Legal Awareness: Nurses should be educated on the ethical and legal implications of AI in clinical practice to ensure responsible use.

This study reveals that although nurses have a general awareness of AI in clinical settings, their practical knowledge and experience remain limited. The majority of respondents exhibited a neutral attitude toward AI, indicating a need for further education and exposure to AI applications in nursing practice. Given the increasing role of AI in healthcare, structured training programs and practical implementation strategies are essential to enhance nurses’ confidence and competence in using AI technology.

• Expanded Sample Size: Future studies should include a larger sample size across multiple healthcare institutions to validate findings.
• Longitudinal Studies: Research should assess how attitudes and knowledge about AI evolve over time with exposure and training.
• Effectiveness of AI Training: Studies should evaluate the impact of AI training programs on nurses’ confidence, knowledge, and clinical decision-making.
• AI Implementation Challenges: Research should explore the barriers and facilitators to AI adoption in nursing practice.
• Patient Outcomes: Future research should assess how AI integration in nursing impacts patient care, efficiency, and safety.

1. Rony MKK, Kayesh I, Bala SD, Akter F, Parvin MR. Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals-A descriptive qualitative study. Heliyon. 2024; 10: e25718.
2. Pailaha AD. The Impact and Issues of Artificial Intelligence in Nursing Science and Healthcare Settings. In SAGE Open Nursing. 2023; 9.
3. Shang Z. A Concept Analysis on the Use of Artificial Intelligence in Nursing. Cureus. 2021; 13: e14857.
4. Rony MKK, Parvin MR, Ferdousi S. Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future. Wiley. 2023; 11.
5. Johnk J, Weibert M, Wyrtki K. Ready or Not, AI Comes - An Interview Study of Organizational AI Readiness Factors. Business and Information Systems Engineering. 2020; 63: 5-20.
6. Morrison K. Artificial intelligence and the NHS: a qualitative exploration of the factors influencing adoption. Future Healthc J. 2021; 8: e648-e654.
7. Alami H, Lehoux P, Denis J, Motulsky A, Petitgand C, Savoldelli M, et al. Organizational readiness for artificial intelligence in health care: insights for decision-making and practice. J Health Organization and Management. 2020; 35: 106-114.
8. Pesapane F, Bracchi DA, Mulligan JF, Linnikov A, Maslennikov O, Lanzavecchia MB, et al. Legal and Regulatory Framework for AI Solutions in Healthcare in EU, US, China, and Russia: New Scenarios after a Pandemic. Radiation. 2021; 1: 261-276.
9. Alqahtani N, Alshahrani S. Exploring nurses' awareness and attitudes toward artificial intelligence: Implications for nursing practice. Digital Health. 2024; 10: 1-10.
10. AI-Qerem W, Eberhardt J, Jarab A, Bawab AQA, Hammad A, Alasmari F. Knowledge and attitudes toward artificial intelligence in nursing: A cross-sectional study. BMC Medical Informatics and Decision Making. 2023; 23: 1-12.
11. Gaffar DA, Alghamdi MS. Assessing nurses' knowledge regarding the application of artificial intelligence in healthcare. Nursing Research and Practice. 2023; 9371969.
12. Khasawneh R, Al-Natour A. Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions' students in Jordan: A cross-sectional study. BMC Medical Education. 2024; 24: 64.
13. Rahman MA, Anwar H. Demographic factors, knowledge, attitude, and perception and their relationship with the intention to use artificial intelligence among nursing students in Arab countries. BMC Medical Education. 2023; 23: 1-14.