Latest / 1 More Paper / Perceptions and Readiness for Generative Artificial Intelligence Implementation Among Oncology Nurses: A Qualitative Study in a Specialized Cancer Hospital — Han et al. (2026)
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- You’re listening to “Perceptions and Readiness for Generative Artificial Intelligence Implementation Among Oncology Nurses: A Qualitative Study in a Specialized Cancer Hospital,” by J. Han and colleagues. Published in 2026. Contents lists available at ScienceDirect Asian Nursing Research journal homepage: the linked source Research Article Jiyoung Han, 1 Kum Hei Ryu, 2 Miyoung Kim, 3 Kwangsoo Shin 4, 1 National Cancer Center, Goyang 10408, South Korea 2 Center for Cancer Prevention and Detection, National Cancer Center, Goyang 10408, South Korea 3 Nursing Department, National Cancer Center, Goyang 10408, South Korea 4 Graduate School of Public Health and Healthcare Management, Catholic Institute for Public Health and Healthcare Management, Songeui Medical Campus, The Catholic University of Korea, Seoul, South Korea a r t i c l e i n f o s u m m a r y Purpose: In order to bridge the gap between rapid technological changes and the healthcare environment, and to alleviate the burden of nursing tasks, the need for change management must be recognized. Understanding nurses' perceptions of generative artificial intelligence (AI) is essential for the successful implementation of nursing change management. However, existing studies are mostly exploratory in nature, focusing on the utility and applications of generative AI technology. There is a lack of research on its implications for the nursing environment and nursing management. The purpose of this study is to explore in depth the perceptions of nurses at cancer specialty hospitals regarding the application of generative AI, as well as the environmental and contextual factors involved. Methods: This study conducted in-depth interviews with nurses at National Cancer Center, the first cancer specialty hospital in South Korea, from July to August 2024. The semistructured questionnaire was developed based on clinical nursing practice guidelines, and participants were selected considering the various charac-teristics and situations of nursing tasks. Results: First, nurses at the cancer specialty hospital recognized change management factors in the areas of ‘basic nursing,’ ‘therapeutic intervention,’ ‘nursing during examinations,’ ‘infection control,’ and ‘nursing administration.’ Second, limitations of change management such as ‘decline in nursing proactivity,’ ‘difficulties in building rapport with patients,’ ‘difficulties in providing personalized nursing care,’ ‘patient information breach’ and ‘unclear accountability’ were identified. Third, preparation strategies for change management such as ‘nurse competency improvement,’ ‘legal protection,’ ‘establishing standardized processes,’ and ‘awareness improvement’ were identified. Conclusion: Based on the research findings, academic and practical implications for the application of generative AI in nursing and strategies to enhance nurses’ readiness for its implementation are presented. © 2026 Korean Society of Nursing Science. Published by Elsevier BV. This is an open access article under the CC BY license (the linked source). Article history: Received 27 February 2025 Received in revised form 18 October 2025 Accepted 20 November 2025 Introduction. For technological changes to benefit organizations, awareness of change management and institutional readiness are essential. These factors ensure that technological advancements contribute practical value beyond economic gains. This approach can play a critical strategic role in promoting shared prosperity within the healthcare system. Digital technologies, such as artificial intelligence (AI), require intentional and focused efforts not only to enhance productivity but also to drive changes in healthcare settings while protecting the interests of healthcare providers and p1976-1317 e2093-7482/© 2026 Korean Society of Nursing Science. Published by Elsevier BV. This is an open access article under the CC BY license (the linked source). Correspondence to: Kwangsoo Shin, Graduate School of Public Health and Healthcare Management, Catholic Institute for Public Health and Healthcare Management, Songeui Medical Campus, The Catholic University of Korea, Seoul 06691, South Korea citizens. This underscores the need for change management to address significant technological transformations as organizational outcomes. AI, as a key driver of digital transformation, is playing a transformative role in healthcare services beyond prediction and automation. Among these, generative AI technologies, such as the generative pre-trained transformer model developed by OpenAI, are transforming healthcare settings by serving as tools for early diagnosis of patient diseases, predicting potential issues, and simulating appropriate treatment methods. It plays a critical role in the diagnosis of various pathological conditions and cancers and is also utilized to promote equitable access to healthcare services [4—8]. Furthermore, intelligent agents (AI agents) are revolutionizing healthcare environments in fundamentally different ways from traditional AI by serving as biomarkers to predict mortality in sepsis patients. Innovative change is un-derstood as the process of embracing new technologies and appropriately applying them in healthcare settings. Change management is essential in nursing practice as well, and generative AI can replace routine tasks performed by nurses and enhance the efficiency of nursing workflows. By automating tasks such as monitoring vital signs, which are both core nursing duties and traditionally time-intensive, nurses can focus more on direct patient care, thereby enhancing the continuity of treatment. The time-consuming task of manual nursing documentation can be made more efficient with the help of voice-based generative AI. Through nursing support such as predicting falls, triaging patients as needed, and forecasting discharge outcomes, generative AI enhances work efficiency and improves the quality of interactions with patients [16—18]. In oncology nursing, individualized care is essential for managing cancer patients' pain, infections, and nutri-tion, and generative AI has been reported to enable such personalized support. Accordingly, cancer specialty hospitals, where the complexity and need for individualized nursing are maximized, represent an optimal setting to explore the potential and limitations of generative AI adoption. As routine nursing tasks become automated and new opportunities emerge through generative AI, a fundamental reassessment of the nurse's role and nursing change management is required. Previous studies have primarily focused on the technical utility of generative AI for specific diseases or on the usefulness of generative AI in nursing education and administration. However, these studies are exploratory in nature, aiming to investigate the utilization of generative AI in nursing practice. Although frontline staff perceptions are critical in technology diffusion and adoption, research has rarely explored how nurses perceive generative AI and its contextual factors. Furthermore, little is known about the specific applicability and practical requirements of generative AI adoption in cancer specialty hospitals, where the medical environment, patient care approaches, and clinical processes are distinct. Given the importance of nurses' leadership in nursing change management, it is crucial and urgent to conduct research that explores cancer specialty hospital nurses' perceptions of generative AI, the challenges of change, and the requirements for their implementation. This study aims to analyze how nurses in cancer specialty hospitals perceive the application of generative AI and explore their cognitive representations and experiences related to nursing change management. This study applied Colaizzi's phenomenological methodology, intentionally sampling and interviewing nurses from cancer specialty hospitals. Colaizzi's phenomenological methodology describes the essential structure of shared experiences by restating the experiential statements of individual study participants into a generalized form. Understanding how nurses in cancer specialty hospitals cognitively represent generative AI and their emotional responses can support the effective application of generative AI in nursing practice. Furthermore, it is anticipated that this understanding can provide insights to guide the implementation of nursing change management in ways that promote the public good. The aim of this study is to explore how nurses in cancer specialty hospitals perceive the application of generative AI and to identify the essential meanings of nursing change management using Colaizzi's phenomenological methodology. Background and literature Digital transformation in healthcare and the emergence of generative AI With the continuous advancement of technology, AI is transforming various industries, including the healthcare sector. From clinical decision support to administrative automation, AI con-tributes to improving treatment outcomes, reducing costs, and accelerating advancements in healthcare. In particular, generative AI, a type of generative model, enhances clinical practice by supporting rare disease research and drug discovery. It also improves clinical decision-making through the detection of signs and diseases and data-driven diagnoses. Generative AI-based virtual health assistants also hold the potential to revolu-tionize patient care methods. Although prior research on the digital transformation of healthcare and the emergence of generative AI remains limited, Reddy reported that generative AI technologies have the potential to bring innovation to the healthcare sector by offering automated systems, enhanced clinical decision-making, and personalized recommendations. Thakur identified generative AI as a transformative technology in healthcare, driving advancements in mental health, drug discov-ery, and diagnostic improvement. Technology drives change management in the operational as-pects of organizations. Generative AI also drives innovation in healthcare organizations, necessitating management strategies to address changes in healthcare services. To drive organizational change through the adoption of new technologies like generative AI, it is crucial to understand how they integrate with workflows and apply them effectively. DeSanctis and Poole argued that technology is not merely adopted but is shaped within the process of change management as it interacts with an organization's structure, norms, and members. However, the rise of generative AI presents significant challenges to healthcare change management. While generative AI creates new opportunities, it also raises significant challenges, including concerns about patient data protection, technological bias, and ethical use. Particularly, excessive reliance on generative AI technologies poses the risk of hindering the development of critical thinking skills and creativity. Additionally, inaccuracies, mal-functions, or potential misuse may create challenges in managing change. Zhang and Kamel Boulos reported that effective change management requires a thorough understanding of the generative AI being implemented as well as the ability to ensure their accuracy and appropriateness. Yim et al. emphasized the need to provide continuous education and training to ensure a proper understanding and effective utilization of generative AI. Thakur highlighted the need for efforts to minimize potential risks, such as the use of biased data and lack of oversight, when adopting AI from a change management perspective. Nursing change management in the era of generative AI emergence Generative AI is a transformative technology that revolution-izes nursing practice, creating new opportunities. Alderden et al. and An et al. reported that generative AI can classify patients based on disease severity and nursing needs, as well as predict patients requiring transfer to the intensive care unit. Schneider-Kamp and Swan highlighted the potential of generative AI in predicting and monitoring adverse drug reactions. Generative AI has been applied to various predictive tasks, such as monitoring vital signs in real time, supporting fall prevention, and forecasting infection risks and hospitalization outcomes [12,13,44—48]. Generative AI has been reported to aid in psychi-atric nursing care for oral cancer patients. Generative AI has also been reported to be applicable in classifying patients at risk of readmission among those visiting the emergency department and in predicting discharge outcomes. Additionally, generative AI technologies have been shown to promote efficiency in nursing administration and management. Generative AI technologies currently support the more efficient execution of administrative tasks, such as manual and repetitive documentation and patient record management. Generative AI has been reported to assist in management by detecting causes of nursing burnout and job stress, and to be used as a tool to detect and classify levels of bullying among nurses, thereby inno-vatively transforming approaches to nursing management. Furthermore, integrating generative AI technologies into nursing education has been shown to add value to nurses' learning experiences. For example, it has been reported that support through simulations enables diverse and realistic training experiences. It has been shown that generative AI enables individualized learning for students and performance-based education with real-time feedback. However, several challenges exist regarding its acceptance and adoption. While generative AI supports innovative nursing tasks, concerns about reliability, bias, and transparency cause nurses to hesitate in its application. Previous studies have reported that while AI offers advantages in promoting change management in the nursing field, challenges such as resistance to change and ethical complexities remain. Amin et al identified factors such as lack of familiarity with AI technologies, bias in decision-making, technical difficulties, insufficient training, and fear of technology replacing human interaction as barriers to adoption. Lora and Foran reported that nurses express concerns about ethical issues, privacy protection, and the potential decline in nursing professionalism with the use of AI. They reported that strong organizational support, comprehensive training programs, and the implementation of an innovative organizational culture can help address these challenges. Alenezi et al emphasized that for change management in healthcare organizations, it is essential to embrace technological advancements, and leaders must foster an innovation culture and enhance the adaptability of healthcare staff. Corvello emphasized the need to establish systems to address issues such as bias, transparency, and accountability, and to create an organizational culture that en-courages nurses to explore the potential of generative AI. Therefore, change management within the organization is necessary to reinterpret generative AI appropriately for nursing tasks and customize it to meet specific needs. Nursing change management is centered around nurse participation and the creation of an open and innovative organizational culture. It is achieved within an organizational culture that encourages interaction with new technologies, expands nurse autonomy, promotes creative use, and supports problem-solving. Generative AI is not applied uniformly to everyone. It is necessary for the organization to change in a way that allows for the interaction between generative AI, nurses, and the organizational environment. Therefore, it is essential to gather nurses' opinions on the applicability of generative AI, from its introduction to its diffusion, and to prepare for nursing change management. To this end, Ross et al provided an application guide for change management through the current status of AI adoption in the healthcare field. They presented examples of AI tools applicable to nursing tasks, potential challenges and opportunities, and recommendations, while emphasizing the role of nurse managers. This study focuses on exploring strategies for the seamless integration of AI into nursing practice. Method Study design In this study, a semistructured questionnaire consisting of four domains was developed to comprehensively assess cancer specialty hospital nurses' emotions and perceptions regarding the application of generative AI, as well as their cognitive represen-tations of change management. The interview questionnaire was based on evidence-based clinical practice guidelines designed to assist nurses' decision-making, and it was structured around four key areas: basic nursing, therapeutic intervention, nursing during examinations, and infection control as these domains are recognized as fundamental areas of nursing practice in clinical guidelines. Evidence-based clinical practice guidelines are frameworks that guide nurses in the clinical setting to perform the best possible interventions, integrating the nurse's expertise, the patient's preferences and values, and available resources. The clinical nursing practice guidelines provided by the Korean Nurses Association serve as a manual outlining the methods for per-forming nursing practices across various clinical settings, making them essential in nursing practice. The interview questionnaire for this study, as shown in Table 1, was based on clinical nursing practice guidelines and was structured around three main areas: the applicability of generative AI in nursing tasks, considerations for implementation, and strategies for dissemination. The applicability section inquired about the use and application of generative AI in nursing tasks, focusing on both positive and negative effects. Additionally, the considerations for implementation addressed ethical issues, conflicts with healthcare regulations, and the impact on the patient—nurse relationship. Finally, the strategies for dissemination inquired about the preparation of healthcare staff, methods of support, and the necessary education and training. Sample This study was conducted with 17 nurses from National Cancer Center, the first cancer specialty hospital in South Korea, to deeply explore the essence of cancer specialty hospital nurses' perceptions and experiences with generative AI. National Cancer Center, as a cancer specialty hospital, has a unique medical environment, patient care approaches, and clinical processes distinct from those of general hospitals. Therefore, by limiting the study to nurses working in cancer specialty hospital, which are closely related to the research context, the study ensures that the results more accurately reflect the actual conditions of cancer specialty hospitals. In particular, this study limited the sample to nurses from outpatient, inpatient, diagnostic, infection control, and nursing administration departments to capture the differences in nurses' perceptions based on the specific characteristics of nursing tasks in cancer specialty hospital. Outpatient and inpatient nurses were selected to assess their perceptions of basic nursing and therapeutic intervention. Nurses from diagnostic departments, such as the cancer prevention screening center, endoscopy room, and radiology department, were selected as participants for the Table 1 Interview Questionnaire. examination section due to the relatively high volume of diagnostic tasks. For infection control, nurses from the operating room, hematopoietic stem cell transplant unit, and intensive care unit were selected as participants. Additionally, nurses from nursing administration and education were selected to assess their perceptions of nursing administration. To this end, participants were recommended by the Nursing Headquarters, and nurses who voluntarily participated in the study. Researchers' preparation Prior to data collection, the researchers engaged in phenomenological training and reflexive discussions to minimize pre-conceptions and biases, practicing the principle of bracketing. In addition, team discussions were maintained throughout the study to enhance rigor and ensure consistency in data interpretation. Data collection The individual in-depth interviews with the research participants were conducted face-to-face from July 15 to August 2, 2024. The interviews were audio-recorded with the prior consent of the research participants to prevent data omission or errors. Each participant took part in one interview, and each interview lasted between 30 to 60 minutes. The questions were presented sequentially based on the flow of the conversation, and efforts were made to ensure that the participant's responses were not interrupted and to expand the discussion. The interview content of the research participants was transcribed verbatim. This study was conducted after receiving approval from the Clinical Research Ethics Committee of National Cancer Center to ensure the ethical protection of the research participants (IRB No: NCC2024-0053). This study was carried out in accordance with the Standards for Reporting Qualitative Research and achieved confirmability by ensuring credibility, fittingness, and auditability. Data analysis This study analyzed the data using the qualitative research method proposed by Colaizzi. In this study, the applicability of generative AI in nursing tasks as perceived by nurses in cancer specialty hospital was described, and the common meanings and essence of nursing change management were identified. Furthermore, to gain a comprehensive understanding of change management in cancer specialty hospital, Colaizzi's phenomenological methodology was deemed suitable for the research objectives. Colaizzi proposed a six-step data anal-ysis method within the phenomenological approach to identify clear meanings from the descriptions provided by participants and to accurately articulate the essence of the phenomenon. Following Colaizzi's six-step approach: First, the transcripts of all interviews were transcribed verbatim within 24 hours and repeatedly read to achieve a comprehensive understanding of the participants' experiences. Second, significant statements related to the phenomenon were extracted with the assistance of NVivo 12.0 software. Third, these statements were carefully reviewed and refined by four researchers and subsequently reformulated into more general expressions to clarify their meaning. Fourth, these reformulated statements, subthemes were identified and further organized into core themes, allowing for a structured interpretation of the data. Fifth, based on the subthemes and core themes, an exhaustive description of the phenomenon was developed to provide a detailed account of nurses' perceptions and experiences. Sixth, the fundamental structure of the phenomenon was then articulated. In addition, aligned with the study objectives, the results were further categorized, drawing on Ross et al, into factors of change management, limitations of change management, and preparation strategies. To ensure the rigor of the study, the results were also reviewed by four experts: one nurse manager working at a cancer specialty hospital, one physician with a doctoral degree in medicine, one scholar with a doctoral degree in technology management, and one human resource development (HRD) expert with extensive qualitative research experience. Furthermore, as part of participant validation, the research participants were asked to review whether there were any discrepancies with their actual experiences and were invited to provide alternative interpretations for any mis-interpreted parts. Results. Most of the nurses who participated in this study reported having no prior experience using generative AI in nursing tasks. In this study, as shown in Table 3, a total of 83 transcripts yielded 29 significant statements on applicability, 10 on considerations for implementation, and 14 on strategies for dissemination. Based on subtheme statements organized around the topic of generative AI, a total of 16 core themes were derived: 6 on applicability, 5 on considerations for implementation, and 5 on strategies for dissemination. The interview participants consisted of 3 nurses from the outpatient department, 3 nurses from the inpatient department, 3 nurses from the diagnostic section, 4 nurses from the infection control section, and 4 nurses from nursing administration and education. There were 2 male participants and 15 female participants. Among them, 5 nurses held positions of charge nurse or higher, while the rest were general nurses. The participants' ages ranged from 23 to 59 years, with an average age of 36. Their clinical experience varied from 2 to 24 years, with an average clinical experience of 11 years and 5 months (Table 2). Applicability First, in terms of applicability, nurses primarily expressed positive opinions regarding the efficiency, safety, and educational aspects of generative AI in nursing tasks. In basic nursing, it was described as providing “consistent quality education,” “interaction,” “vital signs monitoring,” “classification,” “fall prevention,” and “prevention of record omissions.” Therapeutic intervention was described as including “early detection,” “filtering role,” “suggesting dressing methods,” and “predictability.” For nursing during examinations, it was expressed as “efficient task management.” Infection control was described as “risk reduction,” “accuracy,” and “monitoring usage.” In nursing administration, the applicability of generative AI to nursing tasks is clearly evident in expressions such as “time efficiency and accuracy,” “accessible anytime,” “independence,” “problem-solving on one's own,” and “consistent nursing care delivery.” Thus, key factors of applicability included patient-tailored education, real-time monitoring and problem resolution, prevention of patient safety incidents, improved task accuracy and efficiency, infection prevention, and support for new nurse training. Considerations for implementation Secondly, in the considerations for implementation, nurses primarily expressed negative opinions regarding the interaction of generative AI, accountability, and privacy concerns. They expressed concerns about increased nurse dependence, difficulty in building rapport with patients, potential violations of the duty of care, and the risk of patient-related information being leaked. They identified unclear accountability regarding these issues. These negative perceptions stemmed from both personal experiences and negative information from the media or societal discourse. This fear arises from the uncertainty of control. In other words, the considerations for the implementation of generative AI revealed factors such as a decline in nursing proactivity, difficulties in building rapport with patients, difficulties in providing personalized nursing care, patient information breach, and unclear accountability. Strategies for dissemination Thirdly, nurses provided feedback on the need for education, infrastructure, and the establishment of standardized processes for the diffusion of generative AI. This can be seen through the need for simulation-based training, education or training programs, and ethics education. It is also evident in the expressions such as ‘proactive use,’ ‘refined data,’ ‘legal support,’ and ‘continuous monitoring.’ Thoughts such as ‘standardization of cancer patient treatment processes,’ ‘a welcoming atmosphere,’ and ‘the need for promotion for cancer patients” reflect a desire for generative AI to spread in a controlled manner within nursing practice. Therefore, the considerations for the application of generative AI revealed factors such as nurse competency improvement, legal protection, establishing infrastructure, establishing standardized processes, and awareness improvement. Discussion. This study explored the applicability of generative AI, considerations for implementation, and strategies for dissemination, thereby identifying the factors, limitations, and preparation strategies for change management as shown in [Figure 1]. In terms of change management factors, nurses acknowledged the ability of generative AI to support and transform nursing tasks. As expressed in the meaningful statements earlier, nurses responded that generative AI could help answer patients' questions, monitor intake and output, support fall prevention, and contribute to accident prevention through vital sign checks. They highly valued the role of generative AI in therapeutic nursing, such as assisting with pressure ulcer care and determining nursing priorities. The predictive capabilities of generative AI were seen as beneficial with the potential to enable quick responses to critical risks such as sepsis in cancer patients. Support for organizational effectiveness such as reducing turnover rates, was also emphasized. The interview results confirmed nurses' optimistic views on generative AI, and the applicability of generative AI in supporting and transforming nursing tasks was recognized. These results align with previous studies, which suggest that generative AI is a positive tool that can enhance work efficiency and improve patient treatment outcomes. This finding is consistent with previous studies that report the potential for generative AI to be extended to nursing tasks such as patient monitoring, drug-compatibility assessment, and improving patient care [63—65]. In particular, the results of this study uniquely highlight the applicability of generative AI in nursing tasks within cancer specialty hospitals, where the complexity of diseases and individualized care requirements differ significantly from those in general hospital settings. Furthermore, the nursing change management factors identified in this study, such as increased nursing task independence and reduced turnover rates, suggest that the use of generative AI is also positive for organizational effectiveness. The results of this study reflect nurses' optimistic views on generative AI, which can innovate and transform nursing tasks amidst workforce shortages and increasing workloads. Nurses acknowledged the need for change management, but they also recognized its limitations. Some expressed concerns that generative AI could interfere with building rapport with patients and hinder proactive nursing tasks. They also expressed concerns about the issues arising from standardized responses. Other concerns included recognizing the limitations of change management, such as those related to patient privacy protection and unclear accountability for potential problems. Some of these barriers to nursing change management may be related to unfamiliarity, lack of resources, fear of unknown situations, and unintended potential outcomes. Such negative perceptions can be attributed more to media or societal discourse than to actual experiences. The potential of generative AI to reduce nurses' workload and automate repetitive tasks in cancer specialty hospitals―such as documentation, continuous monitoring of infection-prone patients, and management of complex treatment schedules―was emphasized; however, concerns were also raised about its possible negative impact on building rapport with patients and the decline in proactive nursing care. This is in line with previous studies that mentioned that, while the introduction of generative AI offers advantages in promoting nursing change management, challenges such as resistance to change need to be addressed. These concerns reflect the complex attitudes of nurses toward generative AI, and the results align with findings from a study conducted in United Arab Emirates health organizations. This study reported that, along with nurses' positive perceptions of generative AI, they also have concerns such as fears about privacy issues and the potential decline in professionalism. Therefore, the importance of education that includes basic knowledge of generative AI was emphasized. In this study as well, nurses emphasized the importance of various types of education and organizational support for the effective utilization of generative AI. Comprehensive education on generative AI and organizational support through interactive approaches can reduce fear and uncertainty, increase familiarity, and enhance productivity, thereby increasing the likelihood of generative AI technologies being integrated into nursing tasks. This suggests that in cancer specialty hospitals, where complex and individualized care is required, systematic education and organizational support for generative AI can enhance nurses’ ability to use the technology effectively and contribute to improving patient safety and the quality of care. Despite these limitations, nurses focused their attention on preparations for the successful application of generative AI. They recognized the need for standardized treatment processes and clear differentiation of job roles. They also called for a clear distinction of accountability and the establishment of a legal framework. This perception reflects nurses' demand for a controllable system. In addition to systemic considerations, most nurses emphasized the need for comprehensive education that enables a shift in perception and fosters independent judgment. Before the implementation of generative AI, nurses expressed a desire to have a clear understanding of the technology. Nurse participation is essential in nursing change management. In other words, generative AI should be utilized through the active participation of nurses in its design and regulation, maintaining relationships with patients, and complementing tasks through education on generative AI. Nurses' perceptions of these change management preparation strategies reflect their desire to ensure that generative AI are used effectively in nursing tasks, while also hoping that concerns about control and feelings of powerlessness are addressed. Previous studies have focused on the potential for the diffusion of generative AI through personal or organizational interventions, such as nurse education, the establishment of legal systems, and the development and implementation of technologies tailored to nursing tasks. This highlights the importance of continuous education and organizational roles in addressing the limitations of generative AI and realizing its potential benefits. This study also recognized the importance of basic nursing tasks such as patient assessment skills, which were not identified in previous studies. This reflects the nurses' desire to proactively utilize generative AI rather than relying solely on the technology. Successful nursing change management is possible in a dynamic organizational atmosphere where nurses actively adopt the technology and are supported in doing so. New technologies are adopted in an environment of technology, proactive individual actions, and a flexible organizational atmosphere. Such organizational change management strategies will enable the efficient adoption of technology within the organization and lead to successful digital transformation. Based on the above results, this study has the following academic implications. This study provides an organizational and experiential perspective on nurses' perceptions and readiness for the implementation of generative AI. The results of this study help to understand the factors of nursing change management and facilitate their more effective application in three areas: 1) change management factors, 2) limitations of change management, and 3) preparation strategies for change management. The previously reviewed studies were limited in that they were investigative in nature, focusing on the application of AI in nursing practice based on the current status of AI in the healthcare field. Such studies find it difficult to identify the entire system of generative AI adoption, parts of the system, or specific detailed attributes. The process presented in this study is based on nurses' experiences, making it practical information, and it is related to specific organizational diagnostics such as group problem-solving, motivation, and communication during the generative AI adoption process. It enables questions for diagnosing the integration process of generative AI technologies. This study not only highlights the lack of research focusing on nurses' perceptions of change management from the perspective of cancer specialty hospital nurses but also provides insights for hospital management or nursing ad-ministrators who aim to promote and implement change management. It provides a comprehensive understanding of nurses’ perceptions and readiness for integrating generative AI into nursing practice. This study also presents three practical implications. First, nursing educators should develop educational programs on generative AI. There is a need to integrate educational programs on related advanced technologies such as AI, machine learning, and natural language processing into undergraduate and graduate nursing curricula. This will enable nurses to acquire the knowledge and skills necessary to lead the adoption of generative AI in the healthcare field and promote nursing change management. Nursing educators also need to invest in professional development to be able to teach healthcare informatics effectively. Furthermore, it may be considered to operate a living lab to integrate and refine the acquired knowledge and skills into nursing practice, while developing innovative solutions. It involves establishing a small-scale governance system that allows for the verification of new technologies before they are widely implemented in nursing practice. This approach creates a foundation where nurses, who are at the patient interface, can take the lead in learning and participating in change during the technology adoption process. Such an open environment will contribute to the creation of patient-centered, innovative healthcare services that transcend organizational boundaries. Second, hospital management must encourage the active collaboration of nurses to provide the infrastructure for the proper use of generative AI in nursing practice and to establish standardized processes. The first step in change management is for employees to feel the need for change. It is important to clearly recognize the need of change and instill the awareness that, without change, the organization may be at risk. The biggest ob-stacles to change management are the lack of a sense of urgency and the cynicism of the members. Among some of the participants in this study, negative opinions were expressed, suggesting that generative AI would be difficult to directly assist in nursing tasks and might actually interfere. Ultimately, the success of change management depends on how resistance and indifference are addressed. For the successful implementation of generative AI in nursing tasks, it is essential to encourage members to engage with change and consider new ways of working. In other words, nurses should be involved in the decision-making process of change and be instilled with the belief that change will benefit nursing tasks. Continuous education and training are essential for this. This goes beyond simply understanding generative AI; it should provide practical examples and hands-on experience to support nurses in effectively utilizing generative AI in their work. While factors such as appropriate timing, resources, processes, and authority are important in implementing nursing change management, it is crucial not to forget that employee participation is the most important element. Third, hospitals must foster an open and innovation-friendly organizational culture to promote the awareness and adoption of generative AI. Generative AI is not merely adopted but implemented through a process by which nurses customize and reinterpret it to suit nursing tasks. This process of customization and reinterpretation is a key factor in maximizing the potential of generative AI. The process of customization and reinterpretation is possible within an open organizational culture, whereas a closed and highly controlled organizational culture may pose limitations. Therefore, it is necessary to encourage the use of new technologies and foster an innovative mindset that embraces failure. Nursing change management is achievable within a positive nursing organizational culture that expands nurses' autonomy and promotes innovation. It is important to remember that nursing change management does not depend solely on the new technology itself but on how in-dividuals utilize it in their work and how the organization supports its implementation. Despite these contributions, this study has several limitations. Future research should validate the practical relevance of generative AI by expanding to diverse nursing settings, integrating it into nursing education, and examining its impact on organizational performance and culture. It is necessary to investigate how individual members with varying levels of knowledge and perceptions of generative AI apply and interact with it in their work, as well as the implications this has for organizational performance. Second, the impact of organizational culture on the adoption of generative AI must be examined. Previous studies have reported that organizational culture influences the ways in which generative AI is used and its accessibility. Previous studies have also shown that organizations with hierarchical cultures may face limitations in adopting and applying generative AI. Therefore, it is necessary to explore the application of generative AI across various organizational cultural contexts and investigate whether differences emerge in its adoption and implementation. It is essential to identify how an organization's shared values and norms influence the adoption and interaction with generative AI, and further, to examine their relationship with organizational productivity. Third, it is necessary to ensure objectivity in the adoption of generative AI through empirical data. This study provides in-depth insights into the understanding, concerns, and ex-pectations of generative AI technology as perceived by nurses in a cancer specialty hospital; however, it has limitations in general-izability. Therefore, it is necessary to conduct empirical studies by collecting broader data on nurses' perceptions, applications, and acceptance of generative AI. This will validate the perceptions of nurses identified in this study, enhance the reliability of the research, and provide a broader understanding. Conclusion. The study found that various change management factors were identified across nursing tasks in cancer specialty hospitals. Despite the need for change management, nurses recognized the limitations of such management including difficulties in building rapport with patients and a decline in nursing proactivity. For the successful implementation of generative AI, organizational support and the creation of an innovative organizational culture were emphasized, including comprehensive education, standardized treatment processes, and the establishment of a legal protection. Authors’ contributions Jiyoung Han: Conceptualization; Data curation; Formal anal-ysis; Investigation; Methodology; Project administration; Validation; Resources; Writing-original draft; Review & Editing. Miyoung Kim: Conceptualization; Data curation; Formal anal-ysis; Investigation; Project administration; Supervision; Review & Editing). Kum Hei Ryu: Conceptualization; Data curation; Formal anal-ysis; Investigation; Methodology; Validation; Resources; Writing-original draft; Review & Editing. Kwangsoo Shin: Conceptualization; Data curation; Formal analysis: Project administration; Validation; Resources; Writing-original draft; Supervision; Review & Editing. Consent for publication This study was approved for publication by the Institutional Review Board of the National Cancer Center Korea. Ethics approval and consent to participate The study commenced following approval from the Institutional Review Board of the National Cancer Center Korea (IRB No: NCC2024-0053). Participants voluntarily participated in the study after giving informed consent. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to privacy concerns but are available from the corresponding author on reasonable request. Funding This work was supported by the National Cancer Center Grant (NCC-2432310-1). Conflicts of interest Acknowledgments. This study was supported by the National Cancer Center Grant (NCC-2432310-1). We sincerely thank the nursing staff at the National Cancer Center, Korea, for their valuable participation. Additionally, we appreciate the insightful feedback from the re-viewers, which helped improve this manuscript.