Attitudes of Psychiatric Nurses Towards Artificial Intelligence Applications: A Qualitative Study from China

Attitudes of Psychiatric Nurses Towards Artificial Intelligence Applications: A Qualitative Study from China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Attitudes of Psychiatric Nurses Towards Artificial Intelligence Applications: A Qualitative Study from China Jiangtao Dong, Xin Chen, Chuanwu Lyu, Mingyue Wu, Chengfei Ruan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7950046/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jan, 2026 Read the published version in BMC Nursing → Version 1 posted 16 You are reading this latest preprint version Abstract Background The application of artificial intelligence in mental health faces unique challenges. Psychiatric nurses provide direct care to patients with mental illness, and their attitudes toward artificial intelligence will directly impact the effectiveness of related technologies in clinical practice. Purpose This study aims to explore psychiatric nurses' attitudes toward artificial intelligence applications, identify their needs and expectations, and assess their awareness of potential risks and challenges.. Methods A semi-structured interview approach under qualitative research methodology was employed. Fifteen psychiatric nurses with over two years of clinical experience were recruited from Henan Mental Health Center. Interview data were coded and analyzed using thematic analysis via NVivo 12.0 software. Results Thematic analysis revealed three core themes: Core needs and expectations of psychiatric nurses regarding artificial intelligence; Key perceived risks and challenges of artificial intelligence adoption; Implementation pathways and policy imperatives. Conclusions Psychiatric nurses generally adopt a positive yet cautious stance toward clinical artificial intelligence applications. While they anticipate artificial intelligence to enhance nursing efficiency and patient safety, significant concerns exist regarding ethical issues, compromised humanistic care, and professional role displacement triggered by artificial intelligence. Artificial Intelligence Psychiatric Nursing Qualitative Study Attitudes Figures Figure 1 1 Background Mental disorders constitute a major global health challenge, with a lifetime prevalence rate of 30%, making them one of the main causes of increased disability and mortality worldwide( 1 ). The number of people with mental disorders is increasing in low- and middle-income countries, but there is a severe shortage of treatment and care, while in high-income countries, treatment has not yet reached the minimum standard( 2 ). Traditional psychiatric care relies heavily on subjective clinical experience, which makes it difficult to meet individual needs in the face of challenges such as an increasing number of patients and rising treatment costs( 3 ). Therefore, the shortage of healthcare professionals and the low cure rate of diseases are both significant problems in the field of mental health. Against this backdrop, the development and application of big data and artificial intelligence(AI) offer technological opportunities to address these issues( 4 ). In recent years, the development of AI in the healthcare industry has been rapid, exerting an increasingly significant influence on the medical field and bringing unprecedented opportunities and challenges to clinical nursing( 5 ). AI is defined as the replication of human cognitive processes through machines, including machine learning, deep learning, natural language processing, computer vision, reinforcement learning, and robotics, among other technical means. These technologies can be applied in a series of areas ranging from text generation to clinical practice( 6 ). For example, optimizing the language structure of nursing documents and improving the completeness and clarity of nursing reports( 7 ); generating personalized nursing plans based on clinical problems and providing decision support( 8 ); optimizing the content and execution process of nursing plans through algorithms to enhance service efficiency( 9 ); embedding AI into nursing information systems to achieve near-real-time automated nursing handover records, reducing nurses' administrative burden and enabling them to focus on direct patient care( 10 ). Although advanced diagnostic technologies have not been fully utilized in psychiatric clinical care at present, some initial achievements have been made through the analysis of data using AI and machine learning, such as improvements in diagnosis assessment, disease course prediction, and treatment selection( 11 ). Research shows that integrating AI into clinical care, such as intelligent triage and vital sign monitoring, can significantly improve work accuracy and speed, and reduce labor costs. Particularly in psychiatric care, AI is expected to assist in monitoring high-risk patients and promptly warn of abnormal conditions, thereby improving the patient care experience and safety( 12 ). However, the application of AI in the field of mental health also faces unique challenges. Compared with the field of physical health, the adoption of AI in psychiatry is relatively slow, partly due to the psychological and social complexity and high subjectivity of mental illnesses, which make it difficult for AI to handle the subtle factors involved( 13 ). Moreover, the application of AI in mental health practice has raised a series of ethical and legal issues, including data privacy and security, algorithm bias, and the transparency of AI decision-making processes( 14 ). The humanistic care provided by psychiatric nurses is crucial to the rehabilitation and treatment relationship quality of patients. Over-reliance on AI may weaken this therapeutic relationship, making machine-dominated tasks more challenging in the psychiatric context( 15 ). Besides ethical and humanistic concerns, the application of AI is also limited by technical and institutional factors, such as poor data quality, the absence of standards and regulatory frameworks, and the digital skills gap among healthcare workers( 16 ). Although many healthcare workers hold a positive attitude towards AI, believing it can assist in diagnosis, decision support, and patient management, they generally advocate viewing AI as a partner rather than a replacement to fully leverage the strengths of both AI and clinical experts( 17 ). Therefore, in practical applications, the acceptance of AI by healthcare workers and patients remains a key factor influencing the successful integration of AI technology into healthcare. In summary, existing research and policy frameworks indicate that the application of AI in the mental health field is full of opportunities and challenges. Particularly within China's healthcare context, psychiatric nurses—who engage in direct care of patients with mental disorders—hold attitudes toward AI that directly influence the effectiveness of related technologies in clinical implementation. However, there is still a lack of in-depth exploration of the real thoughts and needs of psychiatric nurses regarding AI applications. This study uses qualitative methods to conduct semi-structured interviews with psychiatric nurses, aiming to gain a deeper understanding of their attitudes towards AI applications, including their expectations, concerns, and suggestions for implementation, to provide a reference basis for the introduction and promotion of AI technology in psychiatric care. 2 Participants and Methods 2.1 Participants This study was conducted at Henan Mental Health Center from May 10 to 15, 2025. The hospital is a national mental health regional medical center in China and also the largest mental health center in Henan Province. Inclusion criteria for participants were: ( 1 ) Being a full-time clinical nursing staff member at the hospital; ( 2 ) Having ≥ 2 years of nursing experience in psychiatric departments; ( 3 ) Providing informed consent to participate. Exclusion criteria comprised: ( 1 ) Intern or visiting nurses; ( 2 ) Those on maternity/medical leave or engaged in extended off-site training programs; ( 3 ) Individuals with severe physical or mental illnesses. Using purposive sampling, a total of 15 respondents were recruited, with a participation rate of 100%. With three nurses representing each of the following departments: Early Intervention Psychiatry, Geriatric Psychiatry, Child and Adolescent Psychiatry, General Psychiatry, Addiction Psychiatry. Additional demographic and professional characteristics of participants are presented in Table 1 . Table 1 Information of the participants Information (n = 15) Frequency ( nl% ) Age (mean ± SD) 32.2 ± 5.65 Years of working experience (mean ± SD) 9.6 ± 5.67 Sex Male 5 (33%) Female 10 (67%) Title Nurse Practitioner 7 (47%) Supervising Nurse 8 (53%) 2.2 Data Collection After obtaining informed consent from all participants, semi-structured interviews were conducted to encourage respondents to express their attitudes towards AI and their thoughts on future AI applications. The following questions were determined after consulting experts and conducting pre-interviews: ( 1 ) What specific assistance do you think the application of artificial intelligence in the future can provide? ( 2 ) What advantages do you think artificial intelligence has in mental health care? ( 3 ) What risks do you think artificial intelligence poses in mental health care? ( 4 ) Have you come into contact with any AI-related tools in your work so far? ( 5 ) What is your attitude or opinion towards the application of artificial intelligence? Why? ( 6 ) How much do you trust artificial intelligence in participating in nursing decision-making? ( 7 ) What's your opinion on the issue of patient privacy protection in the application of artificial intelligence? ( 8 ) What's your view on the issue of responsibility division in the event of artificial intelligence malfunctions? ( 9 ) When more artificial intelligence applications are adopted in the future, what kind of policy support and assistance do you think hospitals or relevant departments should provide? The interviews were conducted by two experienced researchers in a quiet environment. The interviews were terminated when no new concepts emerged during the process. Each interview lasted approximately 40 minutes. During the interviews, researchers utilized electronic devices to record conversation content in real time while carefully observing participants' tone, intonation, pauses, body language, and facial expressions. 2.3 Data Analysis After the interview, the results will be returned to the participants for confirmation. Once all data results have been confirmed to be accurate, the audio content of the interview will be transcribed word for word within 24 hours, and the interviewee's information will be replaced with codes. The transcribed text was manually coded and analyzed sentence-by-sentence using NVivo 12.0 software, then the thematic content was summarized and refined to ensure that there were no omissions, and finally themes and sub-themes were formed. 3 Results Through the thematic analysis of the results of the 15 interviews, three main-thematic categories were distilled in this study, which are as follows: ( 1 ) psychiatric nurses' core needs and expectations of AI; ( 2 ) psychiatric nurses' key risks and challenges to their understanding of AI; ( 3 ) psychiatric nurses' pathways to implementation and policy aspirations regarding AI. Several sub-themes were included under each theme and are reported below. Feedback on the theme was confirmed by five participants through member checking. The frequencies of terms related to each theme are presented in Table 2 .See Fig. 1 for the structure of each theme and sub-theme. Table 2 Frequency of words related to each theme Theme Related words Core needs and expectations Work, Mental, Assistance, Patients, Workload, Alleviation, Nurses, Monitoring, Changes, Analysis Key risks and challenges Mental, Risk, Patients, Damage, Equipment, Data, Leakage, Harm, Issues, Privacy Implementation pathways and policy aspirations AI, Support, Training, National-Level, Healthcare Staff, Work Processes, Resource Provision, Hospitals, Healthcare Policies, Funding Allocation 3.1 Core Needs and Expectations The psychiatric nurses interviewed for this study generally had a trusting and expectant attitude towards the application of AI, which was in line with expectations. For instance, when queried about their stance on AI applications, Early Intervention Nurse 2 noted: "I welcome the early clinical implementation of AI, though I do not believe it can replace humans. In medical domains such as diagnosis, treatment, medication administration, and the interpretation of CT scans and other imaging films, AI may indeed be more accurate than humans."; Child and Adolescent Psychiatry Nurse 2 mentioned: "Definitely still very supportive, it does bring us a lot of convenience in life, like some paperwork processing, recording and so on in the work can provide a lot of ideas, save us a lot of time to access information." 3.1.1 Reducing Workload and Improving Efficiency Interviewed nurses generally hope that AI can take over some of the tedious and repetitive tasks to ease psychiatric manpower tension and reduce work pressure. Many interviewees mentioned that psychiatric wards require frequent rounds and record keeping, and the assistance of an intelligent monitoring system or robot would greatly reduce their physical and time burdens. "For example, it helps nurses to make rounds, monitor patients' vital signs, monitor sleep status, and at night it can help nurses to scan bedside codes, in addition to monitoring whether the patient's mood is normal or not." (Nurse 2, Early Intervention Psychiatry). AI taking on menial tasks in the background is seen as freeing up nurses to spend more time on patient care. Some nurses also expect AI to assist with paperwork completion, data entry, etc., such as intelligent voice entry of nursing records or automatic generation of nursing reports, thus improving overall productivity. "Being able to participate and provide personalized care plans for patients, providing more specific assistance based on different conditions, and also being able to analyze the patient's condition and solve problems based on the patient's test results." (Nurse 3, Child and Adolescent Psychiatry). 3.1.2 Improvement of Nursing Care Quality and Patient Safety The nurses interviewed expected AI-assisted tools to play a role in risk assessment and safety management, helping them to identify potential crises earlier and improve the safety of the clinical care process. For example, some nurses talked about how the introduction of an AI system to predict a patient's risk of suicide or propensity for violence would help to take early interventions to prevent unintended events. "Including the monitoring of the vital signs of patients with mental disorders, the most basic aspect of psychiatric work is that patient safety is more important, and AI is able to monitor some dangerous behaviors of patients or conflicts between patients and stop them in time, or notify us in time to deal with them." (Nurse 3, Early Intervention Psychiatry). In addition, nurses felt that AI's ability to perform precise calculations and continuous monitoring could reduce human omissions and errors, such as automated medication dosage checking and alarms for abnormal vital signs, which could help prevent medication errors and medical mistakes, and improve the accuracy of nursing care and patient safety. "I think it can help the clinic to do some case analysis, can recognize some changes in the patient's condition, and can also help the nursing staff to remind to give medication, turn over and so on." (Nurse 2, Geriatric Psychiatry). 3.1.3 Decision Support and Personalized Care Many nurses hope that AI will become a powerful aid for clinical decision-making, using big data analysis to provide reference suggestions for complex cases and assisting nurses in making more objective and accurate judgments. As psychiatric patients' conditions are variable and assessments are subjective, respondents believe that AI that integrates patient history, clinical symptoms, and behavioral data to provide risk assessment reports or prognostic predictions will help develop more individualized care plans. "I think it can analyze big data and can provide a lot of data support on top of medical diagnosis to assist doctors in diagnosis, as well as can help nurses to optimize nursing care measures." (Nurse 3, Addiction Psychiatry). This intelligent decision support is seen as an effective complement to nurses' clinical experience. Especially when faced with rare and difficult cases, AI can provide specialized knowledge queries and decision-making suggestions to help nurses establish nursing measures. Overall, nurses expect to leverage AI's analytical and predictive capabilities to more accurately match patient needs and provide personalized, anticipatory care. "Being able to provide early identification of mental illness, patient monitoring, mental health, and personalized treatment on top of nursing provides some help." (Nurse 3, General Psychiatry). 3.2 Key Risks and Challenges When it comes to the risks of AI application in the psychiatric field, the nurses' responses mainly include privacy leakage, weakening of humanistic care, and role substitution. 3.2.1 Ethical and Privacy Concerns Interviewees generally expressed concerns about ethical and privacy issues in AI applications. The first is the risk of patient privacy and data security, with many interviewees mentioning that data on psychiatric patients' conditions are highly sensitive, and that if an AI system collects and analyzes a large amount of patient information, it must ensure that this data is not leaked or misused. "I think it's a very rigorous thing, after all, AI is going to use the network, must pay attention to the protection of all the information, because at present, according to the current society, there is a part of the population still discriminate against psychiatric patients, and the information in the cloud must not be leaked out." (Nurse 3, Early Intervention Psychiatry). At the same time, they also questioned the attribution of responsibility in the absence of clear regulations and supervision when AI makes poor decisions that lead to patient harm. "I think this is not well delineated because it involves over many aspects, and this responsibility should be clearly delineated in advance so that it can be a bit more fair." (Nurse 3, Child and adolescent psychiatry). 3.2.2 Weak Humanistic Care Concerns Psychiatric nursing emphasizes interpersonal communication and emotional support, and nurses were concerned that the introduction of AI might weaken the humanistic attributes of nursing. Interviewees believed that listening and empathy are important factors in the efficacy of psychiatric nursing care, while AI lacks emotion and empathy, and may lead to nursing care becoming hard and cold if it is overly reliant on AI. "Too standardized and process-oriented, without human warmth, may be less respectful to patients with mental disorders." (Nurse 1, Addiction Psychiatry). Some nurses mentioned that certain simple chatbots are currently capable of conversing with patients to de-escalate, but still cannot replace human care. A senior nurse mentioned, "I have some concerns about it, will it develop and cause some harm to the patient, especially the psychiatric patient who is like a blank sheet of paper that needs our education and guidance, so will the AI make strange fruits grow on top of this blank sheet of paper like we do." (Nurse 3, Early Intervention Psychiatry). In addition, the nurse mentioned that psychiatric patients often crave interaction with real people, and that patient acceptance and compliance may decline if machines are given too many communication roles. These views reflect the nurses' concern about dehumanization, i.e., they worry that the intervention of AI will turn nursing care into a cold process and cut down on the core value of "person-centeredness" in the therapeutic process. 3.2.3 Trust and Role Replacement Concerns In terms of AI-assisted decision-making, nurses demonstrated a clear crisis of trust and role anxiety. Many interviewees confessed that they dare not fully trust the conclusions given by AI, especially when the AI decision-making process is opaque and unexplained. Nurses trust their own clinical experience and intuitive judgment of patients more, and are unwilling to blindly obey machine recommendations. "It is better to be more cautious in healthcare, after all, it involves patients' life and health, you can use it, but you cannot rely on it." (Nurse 3, Addiction Psychiatry). This distrust stems from the questioning of the reliability of AI algorithms, which suggests that the current AI decision support is not sufficiently interpretable and transparent, resulting in limited trust among nurses. In addition, some nurses developed a sense of professional crisis, fearing that AI developments would undermine the irreplaceability of nurses. "Risk words such as in case AI replaces nurses, do we risk losing our jobs." (Nurse 2, Child and adolescent Psychiatry). While most nurses believe that humanistic care is irreplaceable by AI, the possibility of being replaced by technology for tasks such as primary care and monitoring made them anxious. Overall, the challenge of clarifying the boundaries between the responsibilities of AI and nurses in the team, and utilizing the strengths of AI without weakening the professional status of nurses, is a common concern among nurses. 3.3 Implementation Path and Policy Demands 3.3.1 Strengthening Education and Training and AI Literacy Enhancement In response to the current competency gaps in AI application for healthcare personnel, respondents called for hospitals and authorities to provide systematic and relevant training. They believe that prior to the introduction of AI, a hierarchical education and training should be provided to all nursing staff to enable them to master basic AI principles and operational skills. "Hospitals should provide financial support, as well as training on the level of acceptance inside the wards, good technical training for medical staff, and preferably some small manuals and science for patients." (Nurse 3, General Psychiatric). The training should cover both the use of specific tools and knowledge of the advantages and limitations of AI, risk prevention, etc., in order to improve nurses' rational knowledge of AI. Some nurses also suggested that AI literacy should be included in the assessment for title promotion. "Specific policy support needs to be provided, such as incentives for application and preferential treatment in title promotion." (Nurse 1, Child and adolescent Psychiatry). Overall, a well-developed training system is seen as one of the foundations for the successful implementation of AI, which nurses expect to use to improve their competency and proactively embrace technological change. 3.3.2 Developing Standards and Norms and Strengthening Ethical Regulation Interviewees generally hope that hospitals and industry authorities will introduce clear norms and systems for the clinical application of AI as soon as possible. They pointed out that there is a lack of specific operational guidelines to guide nurses on how they should use AI, in what contexts they should rely on or avoid AI, etc. "The policy aspect is that there is a need for ethical review, now AI may still not be very compatible with some of our policies, the relevant departments still need to assess as soon as possible to be able to allow AI to enter the clinic as soon as possible." (Nurse 3, Early Intervention Psychiatry). For example, which decisions can be handed over to AI assistance and which parts must be guarded by nurses, and the boundaries of the human-machine division of labor need to be defined by regulatory documents. At the same time, nurses emphasized that there should be strict data security and ethical regulatory measures, such as authorization for the use of patient data, regular audits of AI algorithms, and public disclosure of the basis for their decisions. "For example, if AI is to be used in the treatment process, patients must be allowed to have the right to know, so this should be supported by relevant laws." (Nurse 3, Geriatric Psychiatry). In addition, they also hope that the legal and regulatory level will follow up and improve, and clarify the legal responsibility of AI participation in medical behavior. This includes both the access standards and quality regulation of AI products, as well as the basis for determining the responsibility of all parties in the event of medical disputes. "The state should fill in the gaps in the law, and the issue of responsibility division should be clearly delineated so that staff can be better protected." (Nurse 1, Addiction Psychiatry). Therefore, the development of comprehensive standards and ethical and legal frameworks is one of the core demands of nurses for the implementation of AI, and nurses will be willing to try to use AI more boldly with the "double insurance" of the system and the law. 4 Discussion This study explored the attitudes of psychiatric nurses toward AI application through semi-structured interviews, and gained rich insights in the three dimensions of "needs and expectations", "risks and challenges" and "implementation suggestions". A wealth of insights was obtained. Overall, psychiatric nurses are cautiously optimistic about the potential of AI in clinical settings: they expect AI to alleviate current pain points in nursing, but they are also aware of the multiple barriers to implementation. The finding that nurses expect AI to reduce workload and improve efficiency and quality is highly consistent with previous literature reports. Heavy nursing paperwork and custodial tasks have been seen as aspects that can be optimized by AI technology. For example, one study confirmed that integrating AI into nursing workflow can reduce documentation time and significantly save nurses' time spent on documentation, thus allowing nurses to devote more energy to patient care( 18 ). The nurses in this study echoed this direction when they mentioned that they would like AI to automatically generate nursing records and assist in monitoring conditions. In the psychiatric context, nurses are particularly eager for AI to help them identify risks and safeguard safety in a timely manner, and this point is also consistent with the findings of existing studies: AI features such as real-time alerts have been found to improve patient safety and increase the efficiency of care( 3 ). Notably, psychiatric nurses emphasized that AI should be used as a decision-support tool rather than a replacement, which is in line with the views of many experts in the healthcare field. For example, some researchers have emphasized that AI should act as a clinical "partner" to leverage the respective strengths of AI and humans( 19 ). In addition, this study found that nurses expect AI to support their own professional learning and growth, such as acquiring knowledge through ChatGPT and copywriting. The use of generative AI in medical education has attracted attention in recent years, with reviews suggesting that ChatGPT and others show revolutionary potential in nursing education, research writing, and so on, but cautioning the need to be wary of issues such as accuracy and academic integrity( 20 ). The main concerns of nurses revealed in this study (ethical privacy, humanistic care, trust, security, etc.) are echoed in the existing literature. On the ethical level, data privacy and security are recurring dilemmas in healthcare AI applications. Nurses' concerns are justified by the fact that psychiatric patients' information is more sensitive. World health organization (WHO) emphasizes in its AI ethical guidelines that the use of AI must be premised on the protection of patients' privacy and dignity, and suggests that a strict data governance framework should be established( 21 ). Our study supports this view, with nurses explicitly requesting the development of a system to safeguard data security and clarify the attribution of responsibility for AI decision-making, which suggests that there is an urgent need for ethical safeguards at the clinical front-line. In terms of humanistic care, the professional identity of psychiatric nurses relies on the emotional bond they establish with their patients, so they are wary of any technological devices that may weaken the emotional relationship between nurse and patient. Several studies have been conducted to show that mental health practitioners are concerned that AI will negatively affect the therapeutic relationship, and many are resistant to the introduction of machines in therapy( 22 ). This suggests that any AI program rolled out in the psychiatric field must consider how to protect and enhance the humanistic element of care. In addition, regarding career replacement worries, some studies have pointed out that healthcare practitioners generally want AI to be a help rather than a threat, believing that AI can take on tedious tasks and free nurses from transactional work to spend their time on higher-value patient care( 23 ). Nurses' concerns remind administrators that they should pay attention to nurses' psychological feelings and professional orientation issues when advancing AI, and that their anxiety can be alleviated by publicizing and training nurses to understand that AI is a helper rather than a replacement, and by ensuring that caregivers always play an integral role in nursing decision-making in the clinic. At the same time, the multifaceted suggestions made by the interviewees provided new insights. First, education and training for AI adoption was widely recognized as a key measure. Some studies have emphasized that targeted training should be conducted to improve the AI literacy of healthcare workers in order to promote their technological readiness( 24 ). Our study further refined the training needs: nurses wanted training to cover operational skills, ethical risks, and failure prevention plans. This suggests that future AI-related training should be comprehensive, including both instruction on the use of the technology level and conceptual updating at the conceptual level, so that only when nurses truly master AI can the technology be put to its best use. Second, nurses require clear standards and strengthened regulation, which is important at the policy level. In the field of mental health, a recent study systematically reviewed the ethical considerations that need to be attended to in the mental health application of AI and emphasized the importance of developing practice guidelines and norms for its use( 25 ). This study demonstrated nurses' strong expectation for institutional norms to be implemented on the ground, including in-hospital protocols and industry standards. Policy makers should incorporate the views of frontline nurses and balance practicality and operability in system design. For example, for psychiatric AI applications, standardized processes on the use of suicide risk prediction tools and data privacy protection rules can be introduced( 26 ). In terms of policy support, given that AI devices are not yet widely adopted and nurses have a limited understanding of AI, nurses expect greater policy-level guidance and incentives—particularly more targeted refinements for the psychiatric specialty. Such policies should provide financial and personnel support while fostering an innovation-conducive environment. Under the escort of policies and laws, nurses will be more confident and motivated to try AI. 4.1 Limitations This study still has some limitations. First, the sample consisted of only 15 nurses from a tertiary psychiatric hospital, and regional and sample limitations may affect the generalizability of the results. Nurses from different hospital levels and departmental backgrounds may have different levels of awareness and acceptance of AI. Second, qualitative research data relies on the subjective expressions of respondents, which may be subject to social desirability bias. Future studies could be conducted on a larger scale, using either qualitative or quantitative methods, to validate the generalizability of the findings from this study. 4.2 Implications for clinical practice This study has enriched our understanding of the attitudes toward digital health among psychiatric nurses. Compared to previous surveys primarily conducted in Western countries, we found that nurses in the Chinese context have particularly prominent policy and institutional needs regarding AI. This may reflect differences in the degree of reliance on upper-level support among clinical staff across different healthcare systems. Hospital administrators should address nurses' concerns about AI and develop solutions in tandem with AI implementation, such as enhancing data security measures and establishing incident response plans in advance. Additionally, administrators can establish evaluation and feedback mechanisms to ensure that AI use does not compromise patient satisfaction or the quality of humanistic care. Educational departments and hospital training divisions should also develop AI training programs for clinical nurses, covering operational skills, ethical safety, and case simulations, to progressively enhance nurses' digital health capabilities. 5 Conclusion This study offers profound insights into the perspectives and sentiments of the psychiatric nursing community amid the era of AI adoption. The nurses both see the promise of AI for psychiatric nursing, such as reduced burden, assisted decision-making, and safety monitoring, and point to the barriers that must be crossed to achieve these goals, such as ethical challenges, barriers to trust, and limitations in technical conditions. Their suggestions, ranging from strengthening training and improving systems to soliciting participation and support, point the way for promoting the healthy development of AI in mental health care. Looking ahead, more localized empirical studies and practical explorations will help us better understand and meet the needs of AI on the frontline of care, so that AI can truly become a beneficial assistant rather than a potential burden to psychiatric care, and ultimately be able to better serve the physical and mental health of patients. Abbreviations Artificial intelligence AI World health organization WHO Declarations 7.1 Ethics approval and consent to participate This study strictly adhered to the principles of the Declaration of Helsinki (World Medical Association, 2013 revision) for medical research involving human subjects, and complied with the National Ethical Guidelines for Biomedical Research Involving Human Subjects of China. The study protocol was reviewed and approved by the Ethics Committee of Henan Medical University(Reg.No.XYLL-20250473). Prior to data collection, all participating psychiatric nurses were provided with a detailed explanation of the study purpose, procedures, potential risks, and rights. Written informed consent was obtained from each participant, and all interview recordings and transcripts were anonymized—identifying information (e.g., names, workplace details) was replaced with pseudonyms to protect privacy. The storage and analysis of sensitive data strictly followed institutional data security protocols to prevent leakage. 7.2 Consent for publication Not applicable 7.3 Availability of data and materials Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. 7.4 Competing interests The authors declare that they have no competing interests. 7.5 Funding (1) Henan Province Higher Education Teaching Reform Research and Practice Project (Graduate Education Category) - “Research and Practice on High-Quality Development of Key Disciplines in Psychology from the Perspective of Interdisciplinary Integration with Five Parallel Tracks” (2023SJGLX235Y). (2) Henan Provincial Project of Philosophy and Social Sciences for Building an Education-Strong Province (2025JYQS0289). 7.6 Authors' contributions J.T. Dong and D.J. Zhang contributed to the conception and design of the study, and were major contributors in writing the manuscript. F. Yan and X. Chen were responsible for data collection. C.W. Lyu, M.Y. Wu, and C.F. Ruan made substantial revisions to the manuscript. All authors read and approved the final manuscript. 7.7 Acknowledgements The authors of this study would like to express their sincere gratitude to all interviewees for sharing their clinical experiences and insights. Their valuable contributions have provided us with tremendous professional assistance. References Jaeschke K, Hanna F, Ali S, Chowdhary N, Dua T, Charlson F. Global estimates of service coverage for severe mental disorders: findings from the WHO Mental Health Atlas 2017. Global mental health (Cambridge, England). 2021;8:e27. Global regional. and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The lancet Psychiatry. 2022;9(2):137 – 50. Li H, Zhu G, Zhong Y, Zhang Z, Li S, Liu J. Applications of Artificial Intelligence in Psychiatric Nursing: A Scope Review. Studies in health technology and informatics. 2024;315:74–80. Winter NR, Hahn T. [Big Data, AI and Machine Learning for Precision Psychiatry: How are they changing the clinical practice?]. Fortschr Neurol Psychiatr. 2020;88(12):786–93. O'Connor S, Yan Y, Thilo FJS, Felzmann H, Dowding D, Lee JJ. Artificial intelligence in nursing and midwifery: A systematic review. J Clin Nurs. 2023;32(13–14):2951–68. Rony MKK, Parvin MR, Ferdousi S. Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future. Nurs Open. 2024;11(1). Ju H, Park M, Jeong H, Lee Y, Kim H, Seong M, et al. Generative AI-Based Nursing Diagnosis and Documentation Recommendation Using Virtual Patient Electronic Nursing Record Data. Healthc Inf Res. 2025;31(2):156–65. Yadav S. Embracing Artificial Intelligence: Revolutionizing Nursing Documentation for a Better Future. Cureus. 2024;16(4):e57725. Cilla S, Rossi R, Habberstad R, Klepstad P, Dall'Agata M, Kaasa S, et al. Explainable Machine Learning Model to Predict Overall Survival in Patients Treated With Palliative Radiotherapy for Bone Metastases. JCO Clin Cancer Inf. 2024;8:e2400027. Tu YH, Chang TH, Lo YS. Generative AI-Assisted Nursing Handover: Enhancing Clinical Data Integration and Work Efficiency. Stud Health Technol Inf. 2025;329:1928–9. Kolding S, Lundin RM, Hansen L, Østergaard SD. Use of generative artificial intelligence (AI) in psychiatry and mental health care: a systematic review. Acta Neuropsychiatr. 2024;37:e37. Levkovich I. Is Artificial Intelligence the Next Co-Pilot for Primary Care in Diagnosing and Recommending Treatments for Depression? Medical sciences. (Basel Switzerland). 2025;13(1). Dehbozorgi R, Zangeneh S, Khooshab E, Nia DH, Hanif HR, Samian P, et al. The application of artificial intelligence in the field of mental health: a systematic review. BMC Psychiatry. 2025;25(1):132. Sidani L, Nadar SM, Tfaili J, El Rayes S, Sharara F, Elhage JC, et al. Digital Psychiatry: Opportunities, Challenges, and Future Directions. J Psychiatr Pract. 2024;30(6):400–10. Rocheteau E. On the role of artificial intelligence in psychiatry. Br J psychiatry: J mental Sci. 2023;222(2):54–7. Lewin S, Chetty R, Ihdayhid AR, Dwivedi G. Ethical Challenges and Opportunities in Applying Artificial Intelligence to Cardiovascular Medicine. Can J Cardiol. 2024;40(10):1897–906. Briganti G. Artificial Intelligence in Psychiatry. Psychiatria Danubina. 2023;35(Suppl 2):15–9. Michalowski M, Topaz M, Peltonen LM, An. AI-Enabled Nursing Future With no Documentation Burden: A Vision for a New Reality. J Adv Nurs. 2025. Huang R, Li H, Suomi R, Li C, Peltoniemi T. Intelligent Physical Robots in Health Care: Systematic Literature Review. J Med Internet Res. 2023;25:e39786. Zhou Y, Li SJ, Tang XY, He YC, Ma HM, Wang AQ, et al. Using ChatGPT in Nursing: Scoping Review of Current Opinions. JMIR Med Educ. 2024;10:e54297. Yang Y, Cui YU, Wang YT, Xue P, Zhai XM, Qiao YL. [Interpretation of the WHO's Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models and its implications for China]. Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]. 2025;59(6):960–9. Zhang M, Scandiffio J, Younus S, Jeyakumar T, Karsan I, Charow R, et al. The Adoption of AI in Mental Health Care-Perspectives From Mental Health Professionals: Qualitative Descriptive Study. JMIR formative Res. 2023;7:e47847. Hassan EA, El-Ashry AM. Leading with AI in critical care nursing: challenges, opportunities, and the human factor. BMC Nurs. 2024;23(1):752. Sharif L, Almabadi R, Alahmari A, Alqurashi F, Alsahafi F, Qusti S, et al. Perceptions of mental health professionals towards artificial intelligence in mental healthcare: a cross-sectional study. Front Psychiatry. 2025;16:1601456. Wang X, Zhou Y, Zhou G. The Application and Ethical Implication of Generative AI in Mental Health: Systematic Review. JMIR mental health. 2025;12:e70610. Poudel U, Jakhar S, Mohan P, Nepal A. AI in Mental Health: A Review of Technological Advancements and Ethical Issues in Psychiatry. Issues Ment Health Nurs. 2025;46(7):693–701. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Jan, 2026 Read the published version in BMC Nursing → Version 1 posted Editorial decision: Revision requested 25 Nov, 2025 Reviews received at journal 20 Nov, 2025 Reviews received at journal 19 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviews received at journal 14 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 31 Oct, 2025 Reviewers agreed at journal 31 Oct, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviewers invited by journal 29 Oct, 2025 Editor invited by journal 29 Oct, 2025 Editor assigned by journal 27 Oct, 2025 Submission checks completed at journal 27 Oct, 2025 First submitted to journal 26 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7950046","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541912100,"identity":"8cd2bdae-9b52-42d1-80f2-0a1903e68a6e","order_by":0,"name":"Jiangtao Dong","email":"","orcid":"","institution":"Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiangtao","middleName":"","lastName":"Dong","suffix":""},{"id":541912101,"identity":"3eb7f1c2-3380-4793-bfe9-480128719783","order_by":1,"name":"Xin Chen","email":"","orcid":"","institution":"Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Chen","suffix":""},{"id":541912102,"identity":"d3a4110f-67b3-4f1a-a25d-73df58d10789","order_by":2,"name":"Chuanwu Lyu","email":"","orcid":"","institution":"Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chuanwu","middleName":"","lastName":"Lyu","suffix":""},{"id":541912103,"identity":"ff146512-ba89-4eac-85b1-00ee011e2294","order_by":3,"name":"Mingyue Wu","email":"","orcid":"","institution":"Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingyue","middleName":"","lastName":"Wu","suffix":""},{"id":541912104,"identity":"d4b2bf74-a647-406b-a01e-630f9d2f487a","order_by":4,"name":"Chengfei Ruan","email":"","orcid":"","institution":"Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chengfei","middleName":"","lastName":"Ruan","suffix":""},{"id":541912105,"identity":"71e87fa7-0ece-42c7-8604-2255cae2e174","order_by":5,"name":"Fang Yan","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xinxiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Yan","suffix":""},{"id":541912106,"identity":"7ea9636b-7b99-43e5-b714-c1a2130970c5","order_by":6,"name":"Dongjun Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie3QsQrCMBCA4QMhXUJdLwj1FQKFouDDnAidFJzEXYiD9l18hGpAl+jcwaEu4qg4uYjRyanNKJgfMgTuI1wAfL5fLLcHAThrBOuSpj13EoXBciBLkzoSWxzxPBEntakX4S7Tl6469hVSOiWWQ3O+oEoizCHtCHW2pL8tiB8BzX5VSWQxTKRQ+vNKQXgGiSN3koxJaicSl5bEzK4PRA5EGJMAHnTE7Ccj5Smv3SXcLeM7TjRvz4L17fHsRc15Vk1srIVfN143/q5xu7qM+Xw+3//2Aj//TiZrfzioAAAAAElFTkSuQmCC","orcid":"","institution":"Henan Medical University","correspondingAuthor":true,"prefix":"","firstName":"Dongjun","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-10-27 11:08:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7950046/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7950046/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12912-026-04315-6","type":"published","date":"2026-01-16T16:30:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":95662007,"identity":"c9e8be73-51e8-43c5-a2c8-be718c2b0fcc","added_by":"auto","created_at":"2025-11-11 16:37:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":169449,"visible":true,"origin":"","legend":"","description":"","filename":"AttitudesofPsychiatricNursesTowardsArtificialIntelligenceApplicationsAQualitativeStudyfromChina.docx","url":"https://assets-eu.researchsquare.com/files/rs-7950046/v1/1721aa279d0bfe68ec568d79.docx"},{"id":95661844,"identity":"051ce86b-5b72-45f2-99c7-55055dee703e","added_by":"auto","created_at":"2025-11-11 16:36:54","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7899,"visible":true,"origin":"","legend":"","description":"","filename":"2910764a5a294dbebfaf6d1bb2059d07.json","url":"https://assets-eu.researchsquare.com/files/rs-7950046/v1/df5e1a44c61c4ad3714a5358.json"},{"id":95662155,"identity":"e6dbe771-5b71-4353-ae17-1c920bf61a20","added_by":"auto","created_at":"2025-11-11 16:37:13","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84854,"visible":true,"origin":"","legend":"","description":"","filename":"2910764a5a294dbebfaf6d1bb2059d071enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7950046/v1/029a2c1f37cbcd5ac3bd099a.xml"},{"id":95661850,"identity":"cf022d7b-85d4-46bc-83c6-8db11ea2b306","added_by":"auto","created_at":"2025-11-11 16:36:55","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92593,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7950046/v1/1ff56ed781061b8dc63a5d93.jpeg"},{"id":95662307,"identity":"a36d682b-69da-412f-a205-fe35cce628e6","added_by":"auto","created_at":"2025-11-11 16:37:22","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36673,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7950046/v1/70f38e13627b22737ad814a9.png"},{"id":95662296,"identity":"78bd2be5-58f6-4d8c-b130-1e062a872a43","added_by":"auto","created_at":"2025-11-11 16:37:21","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83048,"visible":true,"origin":"","legend":"","description":"","filename":"2910764a5a294dbebfaf6d1bb2059d071structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7950046/v1/e296bc2cdb09be72dfba7053.xml"},{"id":95661997,"identity":"01fdb2e7-e27f-41b2-ad15-2f1db680527d","added_by":"auto","created_at":"2025-11-11 16:37:03","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92322,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7950046/v1/e8e51c5c4b79565488a3ee02.html"},{"id":95661852,"identity":"cc165987-af55-4793-ac43-a93f950f35d7","added_by":"auto","created_at":"2025-11-11 16:36:55","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132456,"visible":true,"origin":"","legend":"\u003cp\u003eTheme and sub-theme structure diagram.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7950046/v1/9e69fee5f318991356e2900e.jpeg"},{"id":100616480,"identity":"4ed76db8-d503-497c-a993-6d3bd726949e","added_by":"auto","created_at":"2026-01-19 17:43:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":954262,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7950046/v1/82dbf09c-c577-4a19-a008-eb0a6d7e0049.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAttitudes of Psychiatric Nurses Towards Artificial Intelligence Applications: A Qualitative Study from China\u003c/p\u003e","fulltext":[{"header":"1 Background","content":"\u003cp\u003eMental disorders constitute a major global health challenge, with a lifetime prevalence rate of 30%, making them one of the main causes of increased disability and mortality worldwide(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The number of people with mental disorders is increasing in low- and middle-income countries, but there is a severe shortage of treatment and care, while in high-income countries, treatment has not yet reached the minimum standard(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Traditional psychiatric care relies heavily on subjective clinical experience, which makes it difficult to meet individual needs in the face of challenges such as an increasing number of patients and rising treatment costs(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Therefore, the shortage of healthcare professionals and the low cure rate of diseases are both significant problems in the field of mental health. Against this backdrop, the development and application of big data and artificial intelligence(AI) offer technological opportunities to address these issues(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn recent years, the development of AI in the healthcare industry has been rapid, exerting an increasingly significant influence on the medical field and bringing unprecedented opportunities and challenges to clinical nursing(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). AI is defined as the replication of human cognitive processes through machines, including machine learning, deep learning, natural language processing, computer vision, reinforcement learning, and robotics, among other technical means. These technologies can be applied in a series of areas ranging from text generation to clinical practice(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). For example, optimizing the language structure of nursing documents and improving the completeness and clarity of nursing reports(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e); generating personalized nursing plans based on clinical problems and providing decision support(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e); optimizing the content and execution process of nursing plans through algorithms to enhance service efficiency(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e); embedding AI into nursing information systems to achieve near-real-time automated nursing handover records, reducing nurses' administrative burden and enabling them to focus on direct patient care(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough advanced diagnostic technologies have not been fully utilized in psychiatric clinical care at present, some initial achievements have been made through the analysis of data using AI and machine learning, such as improvements in diagnosis assessment, disease course prediction, and treatment selection(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Research shows that integrating AI into clinical care, such as intelligent triage and vital sign monitoring, can significantly improve work accuracy and speed, and reduce labor costs. Particularly in psychiatric care, AI is expected to assist in monitoring high-risk patients and promptly warn of abnormal conditions, thereby improving the patient care experience and safety(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, the application of AI in the field of mental health also faces unique challenges. Compared with the field of physical health, the adoption of AI in psychiatry is relatively slow, partly due to the psychological and social complexity and high subjectivity of mental illnesses, which make it difficult for AI to handle the subtle factors involved(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Moreover, the application of AI in mental health practice has raised a series of ethical and legal issues, including data privacy and security, algorithm bias, and the transparency of AI decision-making processes(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The humanistic care provided by psychiatric nurses is crucial to the rehabilitation and treatment relationship quality of patients. Over-reliance on AI may weaken this therapeutic relationship, making machine-dominated tasks more challenging in the psychiatric context(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Besides ethical and humanistic concerns, the application of AI is also limited by technical and institutional factors, such as poor data quality, the absence of standards and regulatory frameworks, and the digital skills gap among healthcare workers(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Although many healthcare workers hold a positive attitude towards AI, believing it can assist in diagnosis, decision support, and patient management, they generally advocate viewing AI as a partner rather than a replacement to fully leverage the strengths of both AI and clinical experts(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Therefore, in practical applications, the acceptance of AI by healthcare workers and patients remains a key factor influencing the successful integration of AI technology into healthcare.\u003c/p\u003e\u003cp\u003eIn summary, existing research and policy frameworks indicate that the application of AI in the mental health field is full of opportunities and challenges. Particularly within China's healthcare context, psychiatric nurses\u0026mdash;who engage in direct care of patients with mental disorders\u0026mdash;hold attitudes toward AI that directly influence the effectiveness of related technologies in clinical implementation. However, there is still a lack of in-depth exploration of the real thoughts and needs of psychiatric nurses regarding AI applications. This study uses qualitative methods to conduct semi-structured interviews with psychiatric nurses, aiming to gain a deeper understanding of their attitudes towards AI applications, including their expectations, concerns, and suggestions for implementation, to provide a reference basis for the introduction and promotion of AI technology in psychiatric care.\u003c/p\u003e"},{"header":"2 Participants and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003eThis study was conducted at Henan Mental Health Center from May 10 to 15, 2025. The hospital is a national mental health regional medical center in China and also the largest mental health center in Henan Province. Inclusion criteria for participants were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Being a full-time clinical nursing staff member at the hospital; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Having\u0026thinsp;\u0026ge;\u0026thinsp;2 years of nursing experience in psychiatric departments; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Providing informed consent to participate. Exclusion criteria comprised: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Intern or visiting nurses; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Those on maternity/medical leave or engaged in extended off-site training programs; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Individuals with severe physical or mental illnesses. Using purposive sampling, a total of 15 respondents were recruited, with a participation rate of 100%. With three nurses representing each of the following departments: Early Intervention Psychiatry, Geriatric Psychiatry, Child and Adolescent Psychiatry, General Psychiatry, Addiction Psychiatry. Additional demographic and professional characteristics of participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInformation of the participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInformation (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (\u003cem\u003enl%\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of working experience (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (33%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (67%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTitle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNurse Practitioner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (47%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSupervising Nurse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (53%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Collection\u003c/h2\u003e\u003cp\u003eAfter obtaining informed consent from all participants, semi-structured interviews were conducted to encourage respondents to express their attitudes towards AI and their thoughts on future AI applications. The following questions were determined after consulting experts and conducting pre-interviews: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) What specific assistance do you think the application of artificial intelligence in the future can provide? (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) What advantages do you think artificial intelligence has in mental health care? (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) What risks do you think artificial intelligence poses in mental health care? (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Have you come into contact with any AI-related tools in your work so far? (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) What is your attitude or opinion towards the application of artificial intelligence? Why? (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) How much do you trust artificial intelligence in participating in nursing decision-making? (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) What's your opinion on the issue of patient privacy protection in the application of artificial intelligence? (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) What's your view on the issue of responsibility division in the event of artificial intelligence malfunctions? (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) When more artificial intelligence applications are adopted in the future, what kind of policy support and assistance do you think hospitals or relevant departments should provide?\u003c/p\u003e\u003cp\u003eThe interviews were conducted by two experienced researchers in a quiet environment. The interviews were terminated when no new concepts emerged during the process. Each interview lasted approximately 40 minutes. During the interviews, researchers utilized electronic devices to record conversation content in real time while carefully observing participants' tone, intonation, pauses, body language, and facial expressions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data Analysis\u003c/h2\u003e\u003cp\u003eAfter the interview, the results will be returned to the participants for confirmation. Once all data results have been confirmed to be accurate, the audio content of the interview will be transcribed word for word within 24 hours, and the interviewee's information will be replaced with codes. The transcribed text was manually coded and analyzed sentence-by-sentence using NVivo 12.0 software, then the thematic content was summarized and refined to ensure that there were no omissions, and finally themes and sub-themes were formed.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eThrough the thematic analysis of the results of the 15 interviews, three main-thematic categories were distilled in this study, which are as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) psychiatric nurses' core needs and expectations of AI; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) psychiatric nurses' key risks and challenges to their understanding of AI; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) psychiatric nurses' pathways to implementation and policy aspirations regarding AI. Several sub-themes were included under each theme and are reported below. Feedback on the theme was confirmed by five participants through member checking. The frequencies of terms related to each theme are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for the structure of each theme and sub-theme.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFrequency of words related to each theme\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTheme\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRelated words\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCore needs and expectations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWork,\u0026nbsp;Mental,\u0026nbsp;Assistance,\u0026nbsp;Patients,\u0026nbsp;Workload,\u0026nbsp;Alleviation,\u0026nbsp;Nurses,\u003c/p\u003e\u003cp\u003eMonitoring,\u0026nbsp;Changes,\u0026nbsp;Analysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKey risks and challenges\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMental,\u0026nbsp;Risk,\u0026nbsp;Patients,\u0026nbsp;Damage,\u0026nbsp;Equipment,\u0026nbsp;Data,\u0026nbsp;Leakage,\u0026nbsp;Harm,\u003c/p\u003e\u003cp\u003eIssues,\u0026nbsp;Privacy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImplementation pathways and policy aspirations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAI, Support, Training, National-Level, Healthcare Staff, Work Processes, Resource Provision, Hospitals, Healthcare Policies, Funding Allocation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Core Needs and Expectations\u003c/h2\u003e\u003cp\u003eThe psychiatric nurses interviewed for this study generally had a trusting and expectant attitude towards the application of AI, which was in line with expectations. For instance, when queried about their stance on AI applications, Early Intervention Nurse 2 noted: \"I welcome the early clinical implementation of AI, though I do not believe it can replace humans. In medical domains such as diagnosis, treatment, medication administration, and the interpretation of CT scans and other imaging films, AI may indeed be more accurate than humans.\"; Child and Adolescent Psychiatry Nurse 2 mentioned: \"Definitely still very supportive, it does bring us a lot of convenience in life, like some paperwork processing, recording and so on in the work can provide a lot of ideas, save us a lot of time to access information.\"\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Reducing Workload and Improving Efficiency\u003c/h2\u003e\u003cp\u003eInterviewed nurses generally hope that AI can take over some of the tedious and repetitive tasks to ease psychiatric manpower tension and reduce work pressure. Many interviewees mentioned that psychiatric wards require frequent rounds and record keeping, and the assistance of an intelligent monitoring system or robot would greatly reduce their physical and time burdens. \"For example, it helps nurses to make rounds, monitor patients' vital signs, monitor sleep status, and at night it can help nurses to scan bedside codes, in addition to monitoring whether the patient's mood is normal or not.\" (Nurse 2, Early Intervention Psychiatry).\u003c/p\u003e\u003cp\u003eAI taking on menial tasks in the background is seen as freeing up nurses to spend more time on patient care. Some nurses also expect AI to assist with paperwork completion, data entry, etc., such as intelligent voice entry of nursing records or automatic generation of nursing reports, thus improving overall productivity. \"Being able to participate and provide personalized care plans for patients, providing more specific assistance based on different conditions, and also being able to analyze the patient's condition and solve problems based on the patient's test results.\" (Nurse 3, Child and Adolescent Psychiatry).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Improvement of Nursing Care Quality and Patient Safety\u003c/h2\u003e\u003cp\u003eThe nurses interviewed expected AI-assisted tools to play a role in risk assessment and safety management, helping them to identify potential crises earlier and improve the safety of the clinical care process. For example, some nurses talked about how the introduction of an AI system to predict a patient's risk of suicide or propensity for violence would help to take early interventions to prevent unintended events. \"Including the monitoring of the vital signs of patients with mental disorders, the most basic aspect of psychiatric work is that patient safety is more important, and AI is able to monitor some dangerous behaviors of patients or conflicts between patients and stop them in time, or notify us in time to deal with them.\" (Nurse 3, Early Intervention Psychiatry).\u003c/p\u003e\u003cp\u003eIn addition, nurses felt that AI's ability to perform precise calculations and continuous monitoring could reduce human omissions and errors, such as automated medication dosage checking and alarms for abnormal vital signs, which could help prevent medication errors and medical mistakes, and improve the accuracy of nursing care and patient safety. \"I think it can help the clinic to do some case analysis, can recognize some changes in the patient's condition, and can also help the nursing staff to remind to give medication, turn over and so on.\" (Nurse 2, Geriatric Psychiatry).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3 Decision Support and Personalized Care\u003c/h2\u003e\u003cp\u003eMany nurses hope that AI will become a powerful aid for clinical decision-making, using big data analysis to provide reference suggestions for complex cases and assisting nurses in making more objective and accurate judgments. As psychiatric patients' conditions are variable and assessments are subjective, respondents believe that AI that integrates patient history, clinical symptoms, and behavioral data to provide risk assessment reports or prognostic predictions will help develop more individualized care plans. \"I think it can analyze big data and can provide a lot of data support on top of medical diagnosis to assist doctors in diagnosis, as well as can help nurses to optimize nursing care measures.\" (Nurse 3, Addiction Psychiatry).\u003c/p\u003e\u003cp\u003eThis intelligent decision support is seen as an effective complement to nurses' clinical experience. Especially when faced with rare and difficult cases, AI can provide specialized knowledge queries and decision-making suggestions to help nurses establish nursing measures. Overall, nurses expect to leverage AI's analytical and predictive capabilities to more accurately match patient needs and provide personalized, anticipatory care. \"Being able to provide early identification of mental illness, patient monitoring, mental health, and personalized treatment on top of nursing provides some help.\" (Nurse 3, General Psychiatry).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Key Risks and Challenges\u003c/h2\u003e\u003cp\u003eWhen it comes to the risks of AI application in the psychiatric field, the nurses' responses mainly include privacy leakage, weakening of humanistic care, and role substitution.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Ethical and Privacy Concerns\u003c/h2\u003e\u003cp\u003eInterviewees generally expressed concerns about ethical and privacy issues in AI applications. The first is the risk of patient privacy and data security, with many interviewees mentioning that data on psychiatric patients' conditions are highly sensitive, and that if an AI system collects and analyzes a large amount of patient information, it must ensure that this data is not leaked or misused. \"I think it's a very rigorous thing, after all, AI is going to use the network, must pay attention to the protection of all the information, because at present, according to the current society, there is a part of the population still discriminate against psychiatric patients, and the information in the cloud must not be leaked out.\" (Nurse 3, Early Intervention Psychiatry).\u003c/p\u003e\u003cp\u003eAt the same time, they also questioned the attribution of responsibility in the absence of clear regulations and supervision when AI makes poor decisions that lead to patient harm. \"I think this is not well delineated because it involves over many aspects, and this responsibility should be clearly delineated in advance so that it can be a bit more fair.\" (Nurse 3, Child and adolescent psychiatry).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Weak Humanistic Care Concerns\u003c/h2\u003e\u003cp\u003ePsychiatric nursing emphasizes interpersonal communication and emotional support, and nurses were concerned that the introduction of AI might weaken the humanistic attributes of nursing. Interviewees believed that listening and empathy are important factors in the efficacy of psychiatric nursing care, while AI lacks emotion and empathy, and may lead to nursing care becoming hard and cold if it is overly reliant on AI. \"Too standardized and process-oriented, without human warmth, may be less respectful to patients with mental disorders.\" (Nurse 1, Addiction Psychiatry).\u003c/p\u003e\u003cp\u003e Some nurses mentioned that certain simple chatbots are currently capable of conversing with patients to de-escalate, but still cannot replace human care. A senior nurse mentioned, \"I have some concerns about it, will it develop and cause some harm to the patient, especially the psychiatric patient who is like a blank sheet of paper that needs our education and guidance, so will the AI make strange fruits grow on top of this blank sheet of paper like we do.\" (Nurse 3, Early Intervention Psychiatry). In addition, the nurse mentioned that psychiatric patients often crave interaction with real people, and that patient acceptance and compliance may decline if machines are given too many communication roles. These views reflect the nurses' concern about dehumanization, i.e., they worry that the intervention of AI will turn nursing care into a cold process and cut down on the core value of \"person-centeredness\" in the therapeutic process.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Trust and Role Replacement Concerns\u003c/h2\u003e\u003cp\u003eIn terms of AI-assisted decision-making, nurses demonstrated a clear crisis of trust and role anxiety. Many interviewees confessed that they dare not fully trust the conclusions given by AI, especially when the AI decision-making process is opaque and unexplained. Nurses trust their own clinical experience and intuitive judgment of patients more, and are unwilling to blindly obey machine recommendations. \"It is better to be more cautious in healthcare, after all, it involves patients' life and health, you can use it, but you cannot rely on it.\" (Nurse 3, Addiction Psychiatry). This distrust stems from the questioning of the reliability of AI algorithms, which suggests that the current AI decision support is not sufficiently interpretable and transparent, resulting in limited trust among nurses.\u003c/p\u003e\u003cp\u003eIn addition, some nurses developed a sense of professional crisis, fearing that AI developments would undermine the irreplaceability of nurses. \"Risk words such as in case AI replaces nurses, do we risk losing our jobs.\" (Nurse 2, Child and adolescent Psychiatry). While most nurses believe that humanistic care is irreplaceable by AI, the possibility of being replaced by technology for tasks such as primary care and monitoring made them anxious. Overall, the challenge of clarifying the boundaries between the responsibilities of AI and nurses in the team, and utilizing the strengths of AI without weakening the professional status of nurses, is a common concern among nurses.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Implementation Path and Policy Demands\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Strengthening Education and Training and AI Literacy Enhancement\u003c/h2\u003e\u003cp\u003eIn response to the current competency gaps in AI application for healthcare personnel, respondents called for hospitals and authorities to provide systematic and relevant training. They believe that prior to the introduction of AI, a hierarchical education and training should be provided to all nursing staff to enable them to master basic AI principles and operational skills. \"Hospitals should provide financial support, as well as training on the level of acceptance inside the wards, good technical training for medical staff, and preferably some small manuals and science for patients.\" (Nurse 3, General Psychiatric). The training should cover both the use of specific tools and knowledge of the advantages and limitations of AI, risk prevention, etc., in order to improve nurses' rational knowledge of AI.\u003c/p\u003e\u003cp\u003eSome nurses also suggested that AI literacy should be included in the assessment for title promotion. \"Specific policy support needs to be provided, such as incentives for application and preferential treatment in title promotion.\" (Nurse 1, Child and adolescent Psychiatry). Overall, a well-developed training system is seen as one of the foundations for the successful implementation of AI, which nurses expect to use to improve their competency and proactively embrace technological change.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 Developing Standards and Norms and Strengthening Ethical Regulation\u003c/h2\u003e\u003cp\u003eInterviewees generally hope that hospitals and industry authorities will introduce clear norms and systems for the clinical application of AI as soon as possible. They pointed out that there is a lack of specific operational guidelines to guide nurses on how they should use AI, in what contexts they should rely on or avoid AI, etc. \"The policy aspect is that there is a need for ethical review, now AI may still not be very compatible with some of our policies, the relevant departments still need to assess as soon as possible to be able to allow AI to enter the clinic as soon as possible.\" (Nurse 3, Early Intervention Psychiatry). For example, which decisions can be handed over to AI assistance and which parts must be guarded by nurses, and the boundaries of the human-machine division of labor need to be defined by regulatory documents.\u003c/p\u003e\u003cp\u003eAt the same time, nurses emphasized that there should be strict data security and ethical regulatory measures, such as authorization for the use of patient data, regular audits of AI algorithms, and public disclosure of the basis for their decisions. \"For example, if AI is to be used in the treatment process, patients must be allowed to have the right to know, so this should be supported by relevant laws.\" (Nurse 3, Geriatric Psychiatry). In addition, they also hope that the legal and regulatory level will follow up and improve, and clarify the legal responsibility of AI participation in medical behavior. This includes both the access standards and quality regulation of AI products, as well as the basis for determining the responsibility of all parties in the event of medical disputes. \"The state should fill in the gaps in the law, and the issue of responsibility division should be clearly delineated so that staff can be better protected.\" (Nurse 1, Addiction Psychiatry). Therefore, the development of comprehensive standards and ethical and legal frameworks is one of the core demands of nurses for the implementation of AI, and nurses will be willing to try to use AI more boldly with the \"double insurance\" of the system and the law.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study explored the attitudes of psychiatric nurses toward AI application through semi-structured interviews, and gained rich insights in the three dimensions of \"needs and expectations\", \"risks and challenges\" and \"implementation suggestions\". A wealth of insights was obtained. Overall, psychiatric nurses are cautiously optimistic about the potential of AI in clinical settings: they expect AI to alleviate current pain points in nursing, but they are also aware of the multiple barriers to implementation.\u003c/p\u003e\u003cp\u003eThe finding that nurses expect AI to reduce workload and improve efficiency and quality is highly consistent with previous literature reports. Heavy nursing paperwork and custodial tasks have been seen as aspects that can be optimized by AI technology. For example, one study confirmed that integrating AI into nursing workflow can reduce documentation time and significantly save nurses' time spent on documentation, thus allowing nurses to devote more energy to patient care(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The nurses in this study echoed this direction when they mentioned that they would like AI to automatically generate nursing records and assist in monitoring conditions. In the psychiatric context, nurses are particularly eager for AI to help them identify risks and safeguard safety in a timely manner, and this point is also consistent with the findings of existing studies: AI features such as real-time alerts have been found to improve patient safety and increase the efficiency of care(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Notably, psychiatric nurses emphasized that AI should be used as a decision-support tool rather than a replacement, which is in line with the views of many experts in the healthcare field. For example, some researchers have emphasized that AI should act as a clinical \"partner\" to leverage the respective strengths of AI and humans(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In addition, this study found that nurses expect AI to support their own professional learning and growth, such as acquiring knowledge through ChatGPT and copywriting. The use of generative AI in medical education has attracted attention in recent years, with reviews suggesting that ChatGPT and others show revolutionary potential in nursing education, research writing, and so on, but cautioning the need to be wary of issues such as accuracy and academic integrity(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe main concerns of nurses revealed in this study (ethical privacy, humanistic care, trust, security, etc.) are echoed in the existing literature. On the ethical level, data privacy and security are recurring dilemmas in healthcare AI applications. Nurses' concerns are justified by the fact that psychiatric patients' information is more sensitive. World health organization (WHO) emphasizes in its AI ethical guidelines that the use of AI must be premised on the protection of patients' privacy and dignity, and suggests that a strict data governance framework should be established(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Our study supports this view, with nurses explicitly requesting the development of a system to safeguard data security and clarify the attribution of responsibility for AI decision-making, which suggests that there is an urgent need for ethical safeguards at the clinical front-line. In terms of humanistic care, the professional identity of psychiatric nurses relies on the emotional bond they establish with their patients, so they are wary of any technological devices that may weaken the emotional relationship between nurse and patient. Several studies have been conducted to show that mental health practitioners are concerned that AI will negatively affect the therapeutic relationship, and many are resistant to the introduction of machines in therapy(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This suggests that any AI program rolled out in the psychiatric field must consider how to protect and enhance the humanistic element of care. In addition, regarding career replacement worries, some studies have pointed out that healthcare practitioners generally want AI to be a help rather than a threat, believing that AI can take on tedious tasks and free nurses from transactional work to spend their time on higher-value patient care(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Nurses' concerns remind administrators that they should pay attention to nurses' psychological feelings and professional orientation issues when advancing AI, and that their anxiety can be alleviated by publicizing and training nurses to understand that AI is a helper rather than a replacement, and by ensuring that caregivers always play an integral role in nursing decision-making in the clinic.\u003c/p\u003e\u003cp\u003eAt the same time, the multifaceted suggestions made by the interviewees provided new insights. First, education and training for AI adoption was widely recognized as a key measure. Some studies have emphasized that targeted training should be conducted to improve the AI literacy of healthcare workers in order to promote their technological readiness(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Our study further refined the training needs: nurses wanted training to cover operational skills, ethical risks, and failure prevention plans. This suggests that future AI-related training should be comprehensive, including both instruction on the use of the technology level and conceptual updating at the conceptual level, so that only when nurses truly master AI can the technology be put to its best use. Second, nurses require clear standards and strengthened regulation, which is important at the policy level. In the field of mental health, a recent study systematically reviewed the ethical considerations that need to be attended to in the mental health application of AI and emphasized the importance of developing practice guidelines and norms for its use(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This study demonstrated nurses' strong expectation for institutional norms to be implemented on the ground, including in-hospital protocols and industry standards. Policy makers should incorporate the views of frontline nurses and balance practicality and operability in system design. For example, for psychiatric AI applications, standardized processes on the use of suicide risk prediction tools and data privacy protection rules can be introduced(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In terms of policy support, given that AI devices are not yet widely adopted and nurses have a limited understanding of AI, nurses expect greater policy-level guidance and incentives\u0026mdash;particularly more targeted refinements for the psychiatric specialty. Such policies should provide financial and personnel support while fostering an innovation-conducive environment. Under the escort of policies and laws, nurses will be more confident and motivated to try AI.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Limitations\u003c/h2\u003e\u003cp\u003eThis study still has some limitations. First, the sample consisted of only 15 nurses from a tertiary psychiatric hospital, and regional and sample limitations may affect the generalizability of the results. Nurses from different hospital levels and departmental backgrounds may have different levels of awareness and acceptance of AI. Second, qualitative research data relies on the subjective expressions of respondents, which may be subject to social desirability bias. Future studies could be conducted on a larger scale, using either qualitative or quantitative methods, to validate the generalizability of the findings from this study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Implications for clinical practice\u003c/h2\u003e\u003cp\u003eThis study has enriched our understanding of the attitudes toward digital health among psychiatric nurses. Compared to previous surveys primarily conducted in Western countries, we found that nurses in the Chinese context have particularly prominent policy and institutional needs regarding AI. This may reflect differences in the degree of reliance on upper-level support among clinical staff across different healthcare systems. Hospital administrators should address nurses' concerns about AI and develop solutions in tandem with AI implementation, such as enhancing data security measures and establishing incident response plans in advance. Additionally, administrators can establish evaluation and feedback mechanisms to ensure that AI use does not compromise patient satisfaction or the quality of humanistic care. Educational departments and hospital training divisions should also develop AI training programs for clinical nurses, covering operational skills, ethical safety, and case simulations, to progressively enhance nurses' digital health capabilities.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study offers profound insights into the perspectives and sentiments of the psychiatric nursing community amid the era of AI adoption. The nurses both see the promise of AI for psychiatric nursing, such as reduced burden, assisted decision-making, and safety monitoring, and point to the barriers that must be crossed to achieve these goals, such as ethical challenges, barriers to trust, and limitations in technical conditions. Their suggestions, ranging from strengthening training and improving systems to soliciting participation and support, point the way for promoting the healthy development of AI in mental health care. Looking ahead, more localized empirical studies and practical explorations will help us better understand and meet the needs of AI on the frontline of care, so that AI can truly become a beneficial assistant rather than a potential burden to psychiatric care, and ultimately be able to better serve the physical and mental health of patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eArtificial intelligence\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAI\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWorld health organization\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWHO\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e7.1 Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study strictly adhered to the principles of the\u0026nbsp;Declaration of Helsinki\u0026nbsp;(World Medical Association, 2013 revision) for medical research involving human subjects, and complied with the\u0026nbsp;National Ethical Guidelines for Biomedical Research Involving Human Subjects\u0026nbsp;of China. The study protocol was reviewed and approved by the\u0026nbsp;Ethics Committee of Henan Medical University(Reg.No.XYLL-20250473).\u003c/p\u003e\n\u003cp\u003ePrior to data collection, all participating psychiatric nurses were provided with a detailed explanation of the study purpose, procedures, potential risks, and rights. Written informed consent was obtained from each participant, and all interview recordings and transcripts were anonymized—identifying information (e.g., names, workplace details) was replaced with pseudonyms to protect privacy. The storage and analysis of sensitive data strictly followed institutional data security protocols to prevent leakage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2 Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.3 Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData sharing is not applicable to this article as no datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.4 Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.5 Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1) Henan Province Higher Education Teaching Reform Research and Practice Project (Graduate Education Category) - “Research and Practice on High-Quality Development of Key Disciplines in Psychology from the Perspective of Interdisciplinary Integration with Five Parallel Tracks” (2023SJGLX235Y).\u003c/p\u003e\n\u003cp\u003e(2) Henan Provincial Project of Philosophy and Social Sciences for Building an Education-Strong Province (2025JYQS0289).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.6 Authors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.T. Dong and D.J. Zhang contributed to the conception and design of the study, and were major contributors in writing the manuscript. F. Yan and X. Chen were responsible for data collection. C.W. Lyu, M.Y. Wu, and C.F. Ruan made substantial revisions to the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.7 Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this study would like to express their sincere gratitude to all interviewees for sharing their clinical experiences and insights. Their valuable contributions have provided us with tremendous professional assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJaeschke K, Hanna F, Ali S, Chowdhary N, Dua T, Charlson F. Global estimates of service coverage for severe mental disorders: findings from the WHO Mental Health Atlas 2017. Global mental health (Cambridge, England). 2021;8:e27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlobal regional. and national burden of 12 mental disorders in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. The lancet Psychiatry. 2022;9(2):137\u0026thinsp;\u0026ndash;\u0026thinsp;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi H, Zhu G, Zhong Y, Zhang Z, Li S, Liu J. Applications of Artificial Intelligence in Psychiatric Nursing: A Scope Review. Studies in health technology and informatics. 2024;315:74\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWinter NR, Hahn T. [Big Data, AI and Machine Learning for Precision Psychiatry: How are they changing the clinical practice?]. Fortschr Neurol Psychiatr. 2020;88(12):786\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO'Connor S, Yan Y, Thilo FJS, Felzmann H, Dowding D, Lee JJ. Artificial intelligence in nursing and midwifery: A systematic review. J Clin Nurs. 2023;32(13\u0026ndash;14):2951\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRony MKK, Parvin MR, Ferdousi S. Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future. Nurs Open. 2024;11(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJu H, Park M, Jeong H, Lee Y, Kim H, Seong M, et al. Generative AI-Based Nursing Diagnosis and Documentation Recommendation Using Virtual Patient Electronic Nursing Record Data. Healthc Inf Res. 2025;31(2):156\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYadav S. Embracing Artificial Intelligence: Revolutionizing Nursing Documentation for a Better Future. Cureus. 2024;16(4):e57725.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCilla S, Rossi R, Habberstad R, Klepstad P, Dall'Agata M, Kaasa S, et al. Explainable Machine Learning Model to Predict Overall Survival in Patients Treated With Palliative Radiotherapy for Bone Metastases. JCO Clin Cancer Inf. 2024;8:e2400027.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTu YH, Chang TH, Lo YS. Generative AI-Assisted Nursing Handover: Enhancing Clinical Data Integration and Work Efficiency. Stud Health Technol Inf. 2025;329:1928\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKolding S, Lundin RM, Hansen L, \u0026Oslash;stergaard SD. Use of generative artificial intelligence (AI) in psychiatry and mental health care: a systematic review. Acta Neuropsychiatr. 2024;37:e37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLevkovich I. Is Artificial Intelligence the Next Co-Pilot for Primary Care in Diagnosing and Recommending Treatments for Depression? Medical sciences. (Basel Switzerland). 2025;13(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDehbozorgi R, Zangeneh S, Khooshab E, Nia DH, Hanif HR, Samian P, et al. The application of artificial intelligence in the field of mental health: a systematic review. BMC Psychiatry. 2025;25(1):132.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSidani L, Nadar SM, Tfaili J, El Rayes S, Sharara F, Elhage JC, et al. Digital Psychiatry: Opportunities, Challenges, and Future Directions. J Psychiatr Pract. 2024;30(6):400\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRocheteau E. On the role of artificial intelligence in psychiatry. Br J psychiatry: J mental Sci. 2023;222(2):54\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLewin S, Chetty R, Ihdayhid AR, Dwivedi G. Ethical Challenges and Opportunities in Applying Artificial Intelligence to Cardiovascular Medicine. Can J Cardiol. 2024;40(10):1897\u0026ndash;906.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBriganti G. Artificial Intelligence in Psychiatry. Psychiatria Danubina. 2023;35(Suppl 2):15\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMichalowski M, Topaz M, Peltonen LM, An. AI-Enabled Nursing Future With no Documentation Burden: A Vision for a New Reality. J Adv Nurs. 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang R, Li H, Suomi R, Li C, Peltoniemi T. Intelligent Physical Robots in Health Care: Systematic Literature Review. J Med Internet Res. 2023;25:e39786.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou Y, Li SJ, Tang XY, He YC, Ma HM, Wang AQ, et al. Using ChatGPT in Nursing: Scoping Review of Current Opinions. JMIR Med Educ. 2024;10:e54297.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang Y, Cui YU, Wang YT, Xue P, Zhai XM, Qiao YL. [Interpretation of the WHO's Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models and its implications for China]. Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]. 2025;59(6):960\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang M, Scandiffio J, Younus S, Jeyakumar T, Karsan I, Charow R, et al. The Adoption of AI in Mental Health Care-Perspectives From Mental Health Professionals: Qualitative Descriptive Study. JMIR formative Res. 2023;7:e47847.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHassan EA, El-Ashry AM. Leading with AI in critical care nursing: challenges, opportunities, and the human factor. BMC Nurs. 2024;23(1):752.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSharif L, Almabadi R, Alahmari A, Alqurashi F, Alsahafi F, Qusti S, et al. Perceptions of mental health professionals towards artificial intelligence in mental healthcare: a cross-sectional study. Front Psychiatry. 2025;16:1601456.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang X, Zhou Y, Zhou G. The Application and Ethical Implication of Generative AI in Mental Health: Systematic Review. JMIR mental health. 2025;12:e70610.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePoudel U, Jakhar S, Mohan P, Nepal A. AI in Mental Health: A Review of Technological Advancements and Ethical Issues in Psychiatry. Issues Ment Health Nurs. 2025;46(7):693\u0026ndash;701.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Psychiatric Nursing, Qualitative Study, Attitudes","lastPublishedDoi":"10.21203/rs.3.rs-7950046/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7950046/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe application of artificial intelligence in mental health faces unique challenges. Psychiatric nurses provide direct care to patients with mental illness, and their attitudes toward artificial intelligence will directly impact the effectiveness of related technologies in clinical practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to explore psychiatric nurses' attitudes toward artificial intelligence applications, identify their needs and expectations, and assess their awareness of potential risks and challenges..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA semi-structured interview approach under qualitative research methodology was employed. Fifteen psychiatric nurses with over two years of clinical experience were recruited from Henan Mental Health Center. Interview data were coded and analyzed using thematic analysis via NVivo 12.0 software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThematic analysis revealed three core themes: Core needs and expectations of psychiatric nurses regarding artificial intelligence; Key perceived risks and challenges of artificial intelligence adoption; Implementation pathways and policy imperatives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePsychiatric nurses generally adopt a positive yet cautious stance toward clinical artificial intelligence applications. While they anticipate artificial intelligence to enhance nursing efficiency and patient safety, significant concerns exist regarding ethical issues, compromised humanistic care, and professional role displacement triggered by artificial intelligence.\u003c/p\u003e","manuscriptTitle":"Attitudes of Psychiatric Nurses Towards Artificial Intelligence Applications: A Qualitative Study from China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 16:18:53","doi":"10.21203/rs.3.rs-7950046/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-25T08:04:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T11:39:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-19T19:59:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-18T17:06:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-14T09:19:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19727401169685985412222075530828116370","date":"2025-11-03T11:36:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14390747456387895656497210496745756731","date":"2025-11-03T11:19:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144551461355495165170376893538352971252","date":"2025-10-31T23:54:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173225663674350046529394104090399689498","date":"2025-10-31T11:01:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187967095653695299367912262692585471296","date":"2025-10-30T06:41:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171956562559630551991321971791799194218","date":"2025-10-29T12:15:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-29T10:56:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-29T06:36:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-27T14:26:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-27T14:23:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-10-26T09:54:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce89b033-eb08-4802-9649-bcf89dabda9a","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:10:11+00:00","versionOfRecord":{"articleIdentity":"rs-7950046","link":"https://doi.org/10.1186/s12912-026-04315-6","journal":{"identity":"bmc-nursing","isVorOnly":false,"title":"BMC Nursing"},"publishedOn":"2026-01-16 16:30:57","publishedOnDateReadable":"January 16th, 2026"},"versionCreatedAt":"2025-11-11 16:18:53","video":"","vorDoi":"10.1186/s12912-026-04315-6","vorDoiUrl":"https://doi.org/10.1186/s12912-026-04315-6","workflowStages":[]},"version":"v1","identity":"rs-7950046","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7950046","identity":"rs-7950046","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}