Healthcare Workers’ Perspectives on Artificial Intelligence in Primary Care

Healthcare Workers’ Perspectives on Artificial Intelligence in Primary Care | 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 Healthcare Workers’ Perspectives on Artificial Intelligence in Primary Care Viljaras Reigas, Ingrida Šukienė This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7346278/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background and Objectives: The integration of artificial intelligence (AI) in healthcare has gained increasing attention to enhance clinical decision-making, service efficiency, and accessibility. While global research highlights both the potential and the challenges of AI implementation, little is known about how primary healthcare professionals perceive and experience this technological shift. This study aimed to examine the attitudes, experiences, and perceived barriers among primary healthcare workers regarding the application of AI in their professional practice. Materials and Methods: A cross-sectional survey was conducted among 678 employees from 43 primary healthcare institutions in Lithuania. Data were collected using a structured questionnaire developed based on existing literature and validated through exploratory factor analysis. Six key factors were identified: perceived AI benefits, threat to professional autonomy, personal readiness and motivation, organizational support and training, ethical concerns, and practical experience. Statistical analysis included ANOVA, t-tests, and Spearman correlation to assess differences across demographic and professional groups. Results: Respondents generally expressed a favorable attitude toward AI, especially recognizing its potential to improve care quality and efficiency. The highest scores were observed in perceived AI benefits and individual readiness. However, practical experience with AI was limited. Significant differences emerged across age, professional role, and years of experience: younger and less experienced staff showed greater enthusiasm and openness to AI, while older and more experienced professionals voiced concerns about autonomy and ethical implications. Organizational support and access to training were positively associated with AI acceptance. Conclusions: Primary healthcare workers are positively inclined toward the adoption of AI but face a gap between perceived potential and actual implementation. Successful integration requires tailored training, strong leadership support, and clear ethical frameworks. Addressing individual, organizational, and ethical factors is critical to fostering trust and enabling sustainable AI use in healthcare settings. artificial intelligence healthcare employee attitude primary care 1. Introduction In recent years, the implementation of artificial intelligence (AI) technologies in medicine has been gaining increasing momentum, especially in processes such as clinical decision-making, patient data analysis, application of preventive measures, and healthcare organization. AI solutions are not only a technological innovation—they change professional roles, communication models, and the everyday reality of clinical practice. Although most studies focus on secondary and tertiary level healthcare institutions, primary healthcare is increasingly becoming a field for AI application experiments, especially aiming for efficiency, personalization, and accessibility of services. At the same time, many questions arise regarding the practical application of AI in real clinical settings—employee preparedness, organizational support, ethical dilemmas, and trust in new technologies. Empirical studies show that the acceptance of technology depends not only on its functionality but also on employees' experience, professional identity, and institutional conditions. Despite growing interest, systematic knowledge about employees’ attitudes toward AI in the context of primary healthcare is still lacking. This is especially relevant in the context of smaller countries, where healthcare resources are limited. In this context, this study aims to assess the experience, attitudes, and perceptions of primary healthcare workers regarding the application of AI in their professional practice. The objective of the study is to analyze what factors determine a favorable or cautious attitude toward AI, what practical and ethical barriers arise, and what organizational solutions are needed for effective and responsible AI integration. This study is based on the latest international scientific sources, quantitative methodology, and includes a wide range of respondents from various primary healthcare institutions. It is expected that the results of the study will contribute to better AI implementation policies, organizational decisions, and training planning in the primary healthcare sector. The development of artificial intelligence (AI) technologies is changing the operating models of the primary healthcare sector, especially in areas where decision-making, information analysis, and patient data management are required. In recent years, AI tools have been integrated not only at the secondary or tertiary level but increasingly in primary care, where healthcare professionals have direct and continuous contact with patients. AI’s potential is manifested in various forms—from symptom assessment algorithms and electronic decision support tools to natural language recognition systems that allow for the automation of documentation processes [ 1 ]. One of the most important factors determining the successful integration of AI in primary healthcare is the readiness of employees to accept and apply these technologies. Studies show that primary care physicians, nurses, and other professionals often experience both enthusiasm for the possible assistance in daily practice and anxiety related to potential changes in professional roles, automation of work tasks, or redistribution of responsibilities [ 2 ]. In addition, primary healthcare often faces limited human and technological resources, which may lead to unequal access to AI solutions and their effective use. According to recent research, the digital divide between healthcare institutions became even more pronounced after the COVID-19 pandemic, which accelerated the development of digital solutions [ 3 ]. However, this development has not always been accompanied by appropriate infrastructure development or staff training. AI also changes the nature of the doctor–patient relationship. While algorithms can help in more accurate diagnosis and chronic disease management, there is growing debate about the importance of maintaining human connection and the potential loss of empathy when some functions are transferred to automated systems [ 4 ]. This aspect is particularly relevant in primary healthcare, where patients often expect not only clinical decisions but also emotional support and a long-term relationship with the service provider. Healthcare workers' direct experience with AI systems in primary care is a crucial factor influencing the acceptance and long-term application of technology. Although AI is often presented as an advanced solution that can improve patient care and reduce specialist workload, the actual experience of workers is often mixed, involving a lack of trust, learning difficulties, and changes in work routines [ 5 ]. Empirical studies show that most family doctors and nurses consider AI a useful tool, but they express concerns about the reliability of the technology and its ability to adequately assess complex clinical contexts. AI decision-making systems are often perceived as “black boxes” whose logic is not fully understood even by experienced professionals [ 6 ]. This raises doubts about whether AI recommendations can be trusted, especially when decisions must be made quickly and based on human judgment and intuition. Moreover, some employees report that the implementation of AI has increased not only technological but also psychological pressure. Additional responsibilities have emerged—learning to use new systems, documenting data, and aligning AI recommendations with their clinical judgment [ 7 ]. This burden is especially felt in smaller institutions where workers lack technical support or training. On the other hand, studies show that when AI integration is carried out consistently and with clear employee involvement, it can greatly facilitate clinical decisions. For example, nurses positively evaluate AI-based reminders regarding preventive measures or patient monitoring, especially in chronic disease management [ 8 ]. These functions not only improve work organization but also help ensure higher quality care. It is also observed that employees' attitudes depend on previous experience with technologies—those who have previously been involved in the implementation of digital tools or have been trained to use information systems evaluate the benefits of AI more positively and adapt more quickly. In contrast, less technologically prepared specialists tend to experience greater resistance, fatigue, and even technological stress [ 9 ]. It can be stated that employee experiences are heterogeneous and depend on organizational readiness, quality of training, workload, personal attitudes, and the transparency of the technology. This highlights the need to evaluate not only the functioning of AI systems themselves but also the social and professional context in which they are implemented. Healthcare workers' attitudes toward artificial intelligence in primary healthcare encompass a wide spectrum of emotional, ethical, and professional evaluations. While some professionals view AI as an advanced and useful solution, others associate it with a reduction in professional autonomy, ethical challenges, and anxiety about their future role [ 10 ]. A positive attitude is usually associated with AI's ability to improve work efficiency and the quality of patient care. Professionals value the possibility to use AI for analyzing large amounts of data, predicting disease progression, managing patient flows, or identifying risk factors [ 11 ]. Studies show that family doctors and nurses with experience using AI-based clinical decision support tools often mention reduced administrative burden and improved management of patient flows as key benefits [ 12 ]. However, professional anxiety often coexists with optimism. One of the most frequently expressed concerns is the fear that AI will eventually replace human decision-making roles, and specialists will become mere “executors of technology” [ 13 ]. This is especially felt among older employees or those lacking adequate digital competence. Ethical aspects also raise concerns—employees often question whether AI decisions consider human individuality, context, and values. Automated decisions, despite their accuracy, may be unacceptable when dealing with complex or emotionally sensitive cases [ 14 ]. Especially in primary healthcare, where a long-term relationship with the patient, trust, and confidentiality are important, employees raise questions about ensuring data security and privacy. It is important to note that employee attitudes toward AI also depend on how the technology is introduced at the organizational level. When implementation involves employees, explains goals, and applies individualized training, the attitude is much more favorable. Conversely, when AI is presented as superior to human judgment or introduced without consultation, it causes resistance, distrust, and a technological gap [ 15 ]. The overall attitude of employees toward AI reflects a constant tension between the potential of innovation and the preservation of professional identity. Although most acknowledge that AI can improve care quality and support decision-making, it is essential to ensure that technologies are applied ethically, transparently, and with human care at the center. Therefore, employee involvement in technology planning, ethical decision-making, and continuous professional education become key aspects of successful integration. The implementation of artificial intelligence in healthcare inevitably raises ethical questions, which become particularly relevant in the primary care sector. Here, employees most often face complex decisions requiring not only clinical competence but also ethical sensitivity, empathy, and a personalized approach to the patient. Artificial intelligence, operating based on algorithms and standardized models, risks oversimplifying decision-making, disregarding the unique situation of the patient [ 16 ]. One of the key ethical challenges is the distribution of responsibility. When decisions are made based on AI recommendations, healthcare professionals face the question of who is responsible for an inappropriate decision—the human or the system? [ 17 ]. This uncertainty reduces trust in technology and hinders its application in practice. In addition, there are questions about information transparency—often the justification of AI decisions is limited, which contradicts the principles of informed decision-making and patient autonomy. Another important aspect is data privacy and security. AI systems often rely on large amounts of patient data, which raises the risk of misuse of this data, especially if the technologies are implemented without adequate infrastructure or legal regulation. Employees, especially in nursing and family medicine, express concern about potential data leaks and loss of patient trust [ 18 ]. Organizational culture and attitude toward innovation also determine how successfully AI is accepted. Research shows that organizations investing in employee involvement, leadership, and open communication about changes can ensure more positive technology adoption [ 19 ]. In contrast, hierarchically rigid or resource-limited organizations often face resistance, cynicism, and lack of AI system implementation in daily activities. Learning and preparedness aspects are crucial—without adequate education, employees do not feel competent to use AI solutions. Lacking time, resources, or individualized training, technology adoption becomes a formal and ineffective process. In addition, practical training is often lacking, which would allow understanding not only the technical operation but also the situations in which AI can or should not be applied [ 20 ]. Various studies emphasize that consistent, ongoing, and contextualized training must be an integral part of AI implementation. This is especially relevant in primary healthcare, where employees often work independently, without IT specialists or technology consultants. The ability not only to use the system but also to critically assess its recommendations is an essential element of safe and ethical AI use. In summary, the implementation of AI in primary healthcare cannot be successful if ethical, organizational, and educational components are ignored. Only by aligning technological solutions with the principles of human dignity, safety, responsibility, and professional identity can sustainable and socially acceptable progress be achieved. The conducted literature analysis reveals that the integration of artificial intelligence (AI) into primary healthcare is a complex process requiring not only technological solutions but also social, organizational, and ethical preparedness. Although many studies on AI applications in healthcare have been conducted in recent years, many aspects remain unexplored in the context of primary care, especially related to employee experiences and attitudes. First, the analyzed sources show that healthcare workers have mixed feelings about AI—some see it as an opportunity to improve work efficiency and patient care, while others see it as a threat to professional autonomy and the relationship with the patient. This attitude often depends on digital literacy, previous experience, and organizational support. However, many studies still describe employee attitudes superficially, relying only on general surveys or conceptual evaluations, rather than in-depth analyses or contextualized experiences [ 14 , 21 ]. Second, employees' experiences using AI—especially in the long term—remain poorly documented. Most scientific works analyze the initial implementation of AI or pilot projects, but there is a lack of empirical material on long-term changes in work processes, professional identity, and models of patient care. Moreover, little attention is given to how AI integration affects team dynamics, distribution of responsibilities, and interprofessional collaboration [ 9 , 19 ]. Third, organizational and learning aspects are often cited as major challenges, but this area also lacks consistent data. There is no unified approach to which training models would be most effective, how employees assess the quality of training, or how institution leaders contribute to technological transformation. Some studies emphasize that successful AI integration requires a culturally prepared organization, but specific strategies are often missing [ 16 , 22 ]. Fourth, ethical aspects—such as responsibility, decision transparency, patient autonomy, and data privacy—are mostly discussed at a theoretical level. There is a lack of practical research revealing how these issues are addressed in real primary care practice, what mechanisms are in place, and what changes employees expect or fear. Moreover, the transparency of AI decisions (the “black box” problem) still raises distrust, which reduces the extent of their application [ 6 , 17 ]. Finally, it is noted that most studies focus on doctors’ experiences, while the perspectives of nurses, nurse assistants, or other team members are analyzed much less frequently. Considering that primary healthcare services are provided by multiprofessional teams, it is necessary to broaden the research field to include employees at all levels and assess their interactions with AI solutions. Considering these gaps, it can be stated that a new, empirically based study is needed, focused on the experiences and attitudes of employees working in primary healthcare toward AI. Such a study would help not only to understand practical challenges and expectations but also to form the foundation for sustainable technology implementation strategies that meet employee needs. 2. Materials and Methods This study employed a quantitative cross-sectional research design, which allows for the simultaneous assessment of participants’ attitudes, experiences, and demographic characteristics. The cross-sectional design is particularly suitable when aiming to describe the distribution of opinions in a large population and examine their associations with certain factors [ 23 , 24 ]. The aim of the study was to explore the attitudes and experiences of employees working in primary healthcare institutions related to the application of artificial intelligence (AI) in professional practice. The study sample consisted of 678 employees from 43 primary healthcare institutions in Lithuania, selected using purposive sampling. This method is recommended when it is necessary to include a specific professional group with direct experience of the phenomenon under investigation [ 25 ]. Institutions were selected based on geographic diversity, the nature of their activities, and the volume of patients served. All research activities were carried out only after obtaining official written consent from the management of the institutions. Inclusion criteria was the employee works in a primary healthcare institution (physician, nurse, or other healthcare personnel) and a signed informed consent document. To ensure the appropriateness of the questionnaire, a pilot study was conducted in one institution, involving 32 employees. Pilot studies help assess the clarity, comprehensibility, and structure of the instruments and allow for content adjustments before final implementation [ 26 ]. Based on participants’ feedback, some statements were refined or removed to improve internal consistency and content validity. Data were collected using a structured paper-based questionnaire, completed anonymously at employees’ workplaces. The questionnaire consisted of 30 statements evaluated on a 5-point Likert scale (from “strongly disagree” to “strongly agree”) and 4 demographic questions (gender, age group, position, and work experience). This format is considered appropriate for attitude research as it allows for the quantitative assessment of the strength of attitudes [ 27 ]. The full English version of the questionnaire used in this study is available as Supplementary File 1. The questionnaire was developed based on the latest scientific research on AI applications in healthcare [ 1 , 9 , 15 , 19 , 21 , 22 , 28 ]. The internal consistency of the questionnaire was assessed using the Cronbach’s alpha coefficient. This indicator reflects how reliably the statements measure the same construct—in this case, employees’ attitudes toward the application of artificial intelligence in healthcare. A separate alpha coefficient was calculated for each thematic block, and an overall value was provided for the entire questionnaire (see Table 1 ). Table 1 Cronbach’s alpha coefficients by thematic blocks. Thematic block Cronbach’s alpha Perceived benefits of artificial intelligence 0.82 Anxiety about professional autonomy 0.79 Usage experience and learning opportunities 0.84 Ethical and responsibility aspects 0.76 Organizational support and technological readiness 0.80 Personal readiness to use AI 0.81 Overall questionnaire reliability 0.89 All Cronbach’s alpha results exceed the 0.76 threshold, indicating very good internal consistency of the questionnaire. The highest reliability was demonstrated by the block “Usage experience and learning opportunities” (0.84), and the overall questionnaire coefficient (0.89) confirms that the research instrument was suitable and reliable for measuring employee attitudes. The study was conducted in accordance with the principles of the World Medical Association’s Declaration of Helsinki (2013). The study plan was reviewed by a research and ethics committee (following the SMK example), and the research was carried out only after receiving permission. All participants signed an informed consent form that explained the study's purpose, the voluntary nature of participation, and the assurance of data confidentiality and anonymity. The main principles of bioethics were followed [ 29 ]: non-maleficence & beneficence – the impact on participants was minimal, with no harm; voluntariness and informed consent – participation was voluntary; privacy and confidentiality – no personal data were collected; Justice – equal participation conditions were provided for all employees; Transparency – the goals and process of the study were clearly presented. Statistical analysis was performed using IBM SPSS Statistics software (version 29). The following analyses were applied: Descriptive statistics – mean, median, standard deviation, minimum, and maximum values were calculated; Correlation analysis – Pearson correlation coefficient was used to assess relationships between variables; Reliability – internal consistency was evaluated using Cronbach’s alpha; values above 0.70 were considered acceptable [ 30 ]; Factor analysis – exploratory factor analysis (EFA) was performed to identify the main dimensions of the questionnaire. KMO and Bartlett’s test were used to assess the suitability of the structure; Non-parametric tests – Kruskal–Wallis and Mann–Whitney U tests were applied when data did not meet normality assumptions; Parametric tests – ANOVA and independent samples t-test were used to evaluate group differences. Statistical significance was considered when p < 0.05. 3. Results A total of 678 employees working in primary healthcare institutions participated in the study. The respondents represented various professions, age groups, and levels of work experience. This diverse participant profile allows for a comprehensive assessment of healthcare workers’ attitudes from different perspectives. The composition of the sample shows that most participants were women (82.4%) and nurses (54.3%). More than one-third of respondents indicated having over 20 years of work experience, and the dominant age group was 41–50 years. This suggests that the responses reflect the opinions of experienced professionals. Analysis of respondents’ attitudes toward artificial intelligence. Respondents evaluated 30 statements related to the application of artificial intelligence in primary healthcare. A five-point Likert scale was used for the evaluation (1 – strongly disagree, 5 – strongly agree). The statements were divided into six thematic blocks based on their content. Means and standard deviations for each block were calculated (see Table 2 ). Table 2 Means and standard deviations of thematic blocks of respondents’ attitudes. Thematic block Mean Standard deviation Perceived benefits of artificial intelligence 4.12 0.61 Anxiety about professional autonomy 3.45 0.77 Usage experience and learning opportunities 3.38 0.83 Ethical and responsibility aspects 3.58 0.69 Organizational support and technological readiness 3.62 0.72 Personal readiness to use AI 4.06 0.59 The highest mean was recorded in the thematic block “Perceived benefits of artificial intelligence” (mean = 4.12), indicating that most employees recognize the potential of this technology to improve healthcare services. Personal readiness to use AI was also evaluated positively (mean = 4.06). Meanwhile, the lowest mean was in “Usage experience and learning opportunities” (mean = 3.38), reflecting the need for better preparation and practical training among employees. Exploratory factor analysis. To confirm the construct validity of the questionnaire and identify the main dimensions of employees’ attitudes, an exploratory factor analysis was conducted using the principal components method with Varimax rotation. Prior to that, the data suitability for factor analysis was assessed. The Kaiser–Meyer–Olkin measure (KMO) = 0.902 indicated very good sample adequacy. Bartlett’s test of sphericity: χ² = 6423.51; p < 0.001 confirmed that the correlation matrix significantly differs from the identity matrix and is suitable for factor analysis. The analysis revealed six significant factor groups that together explained 71.2 percent of the total data variance. Each factor was interpreted and named based on the statements with the highest loadings (see Table 3 ). Table 3 Factor names and explained variance. Factor Name Explained variance (%) 1 Perceived benefits of artificial intelligence 17.3 2 Threat to professional autonomy and identity 13.8 3 Personal readiness and motivation to use AI 12.4 4 Organizational support and learning opportunities 10.3 5 Ethical aspects and perception of responsibility 9.1 6 Practical experience of using AI 8.3 Total explained variance 71.2 The factor analysis confirmed the theoretical basis of the questionnaire and identified six semantically clear factor groups: Perceived benefits of artificial intelligence – includes statements about AI’s ability to improve work efficiency, reduce error risk, and increase patient accessibility. Threat to professional autonomy and identity – reflects employees’ concerns about the potential impact of technology on their professional roles, decision-making, and maintaining human connection with patients. Personal readiness and motivation to use AI – relates to individual willingness, curiosity, and openness to applying AI in daily practice. Organizational support and learning opportunities – include evaluations of management communication, availability of technical support, and sufficiency of training. Ethical aspects and perception of responsibility – reveals employees’ views on decision transparency, data protection, and legal uncertainty. Practical experience of using AI – includes frequency of actual use, integration of tools into work processes, and trust in their functionality. The analysis demonstrated a good internal structure of the questionnaire, confirmed the theoretical components of attitudes, and provides a basis for further statistical analysis. The six factors highlight the key aspects influencing the acceptance of artificial intelligence in primary healthcare. Analysis of attitude differences by demographic variables. The study aimed to determine whether employees’ attitudes toward artificial intelligence significantly differed based on their gender, position, age group, and work experience. The data were tested for normality, and depending on the results, t-tests, ANOVA, or the Kruskal–Walli’s test were applied. When comparing the means of males and females across each attitude factor, significant differences were identified in only two: Personal readiness and motivation to use AI and Ethical aspects and perception of responsibility. Male respondents scored slightly higher in these areas (see Table 4 ). Table 4 Attitude differences by gender. Factor Mean (Men) Mean (Women) p-value Perceived AI benefits 4.18 4.11 0.167 Threat to professional autonomy 3.39 3.47 0.286 Personal readiness and motivation 4.19 4.03 0.032 Organizational support and training 3.67 3.61 0.294 Ethical aspects and responsibility perception 3.72 3.55 0.021 Practical AI usage experience 3.42 3.36 0.351 Men's attitudes regarding personal readiness to use AI and their evaluations of ethical aspects were statistically significantly more favorable than women's (p < 0.05), although the differences were not large. Statistically significant differences were found among different professional groups—doctors, nurses, and other specialists—across many aspects of attitudes. Doctors rated AI benefits and its practical applicability higher, while nurses more often expressed concern about professional autonomy (see Table 5 ). Table 5 Attitude differences by professional position. Factor F-value p-value Significance Perceived AI benefits 5.84 0.003 Significant Threat to professional autonomy 6.17 0.002 Significant Personal readiness and motivation 2.79 0.064 Not significant Organizational support and training 4.45 0.012 Significant Ethical aspects and responsibility perception 3.92 0.021 Significant Practical AI usage experience 5.11 0.006 Significant Doctors were more likely to evaluate AI positively and had more experience with these tools. Nurses expressed greater concern regarding the impact of AI on their professional identity and decision-making. Respondents were divided into five age groups: under 30, 31–40, 41–50, 51–60, and 61 and over. One-way ANOVA was used to analyze differences in attitudes toward AI across these groups (see Table 6 ). Table 6 Attitude differences by age group. Factor F-value p-value Significance Perceived AI benefits 2.12 0.078 Not significant Threat to professional autonomy 4.88 0.001 Significant Personal readiness and motivation 3.64 0.007 Significant Organizational support and training 2.89 0.024 Significant Ethical aspects and responsibility perception 1.47 0.204 Not significant Practical AI usage experience 3.05 0.018 Significant Statistically significant differences by age were found in four out of six factors. Older employees (aged 51 and over) more often expressed concern about professional autonomy, while younger respondents (under 40) showed greater personal openness and motivation to use artificial intelligence. Younger employees also more frequently reported having at least minimal practical experience with AI tools. Respondents’ work experience was categorized into four groups: up to 5 years, 6–10 years, 11–20 years, and more than 20 years. One-way ANOVA was applied to assess differences in attitudes toward artificial intelligence between these groups (see Table 7 ). Table 7 Attitude differences by work experience. Factor F-value p-value Significance Perceived AI benefits 3.72 0.012 Significant Threat to professional autonomy 4.15 0.006 Significant Personal readiness and motivation 2.49 0.061 Not significant Organizational support and training 3.38 0.018 Significant Ethical aspects and responsibility perception 1.96 0.118 Not significant Practical AI usage experience 4.02 0.008 Significant Work experience duration was statistically significantly related to several aspects of attitudes. Most of the differences were identified between professionals with less than 10 years and those with more than 20 years of experience. Employees with longer work experience more often expressed concern about changes in professional roles and were more sensitive to the lack of organizational support. Meanwhile, younger colleagues were more likely to evaluate AI benefits positively and more frequently reported real-world use cases. Correlation analysis. To determine the relationships between demographic variables (age, work experience) and employees’ attitudes toward artificial intelligence, Spearman’s correlation coefficients were calculated. The analysis also included attitude factors identified during factor analysis (see Table 8 ). Table 8 Spearman correlations between attitude factors and demographic variables. Attitude Factor Age Work experience Gender (1 – male, 2 – female) Perceived AI benefits -0.18** -0.16** -0.04 Threat to professional autonomy and identity 0.21** 0.24** 0.06 Personal readiness and motivation -0.22** -0.19** -0.11* Organizational support and learning opportunities -0.14* -0.12* -0.03 Ethical aspects and responsibility perception 0.07 0.09 0.10* Practical AI usage experience -0.25** -0.21** -0.02 The correlation analysis revealed significant but weak to moderate associations: Age and work experience were statistically significantly negatively correlated with perceived AI benefits, personal readiness, and practical experience, and positively with the threat to professional autonomy. Gender showed a small but significant correlation only with personal readiness (lower means among women) and evaluation of ethical aspects (women were more concerned about these issues). The analysis of the study data revealed several key trends related to primary healthcare workers’ attitudes toward the application of artificial intelligence (AI) in professional practice. Evaluating 30 statements on a Likert scale and conducting factor analysis revealed that employee attitudes are multidimensional and depend on various personal and organizational factors. Overall, the highest means were found in the factors related to perceived AI benefits and personal willingness to use these technologies. These results suggest that study participants generally recognize the potential of AI to improve the quality, efficiency, and accessibility of healthcare services. In contrast, the lowest-rated aspect—practical AI usage experience—reveals a gap between theoretical favorability and the actual integration of these technologies into daily practice. The suitability of the questionnaire’s construct was confirmed by high reliability indicators. The overall Cronbach’s alpha coefficient was 0.89, and the individual factors exceeded the 0.76 threshold, indicating very good internal consistency. The exploratory factor analysis identified six conceptually grounded factors: (1) perceived AI benefits, (2) threat to professional autonomy and identity, (3) personal readiness and motivation, (4) organizational support and learning opportunities, (5) ethical aspects and responsibility perception, and (6) practical AI usage experience. These six factors explained 71.2% of the total variance, indicating high construct validity. Between-group differences revealed important demographic influences. Statistically significant differences were identified by position, age, and work experience. Doctors were more likely than other specialists to emphasize AI benefits and practical applicability, while nurses more often expressed concern about professional autonomy. Younger and less experienced respondents demonstrated greater openness to innovation, higher confidence in technologies, and more positive evaluations. Older and more experienced professionals more often indicated ethical and professional concerns. The correlation analysis additionally confirmed these trends: age and work experience were significantly negatively correlated with a positive attitude toward AI benefits, readiness, and practical use, and positively with perceived threat to professional autonomy. Gender-related correlations were weak but statistically significant in aspects related to ethical dilemmas and individual readiness to use AI. In summary, the study results show that primary healthcare workers are generally positively inclined toward AI; however, successful integration requires stronger organizational support, targeted training, and ethically clear implementation principles. Attitude differences between professional groups and demographic segments suggest that AI implementation processes must include individualized interventions, considering employees’ experience, age, and professional identity. 4. Discussion The aim of this study was to analyze the experience and attitudes of employees in primary healthcare institutions regarding the application of artificial intelligence (AI) in the healthcare context. The results obtained confirm a trend described in previous scientific studies—most healthcare professionals acknowledge the potential of AI, but its integration still faces practical, psychological, and organizational barriers. According to the study data, the highest-rated factor by employees was the perceived benefits of AI, especially in aspects such as error reduction, support in clinical decision-making, and improved patient accessibility. This aligns with current scientific literature—various studies emphasize that AI technologies can contribute to more efficient service delivery, particularly in the areas of decision support systems and patient queue management [ 31 , 32 ]. Another highly rated factor—personal readiness and motivation to use AI—suggests that employees are open to innovation; however, a clear system and supportive environment are required. This trend has also been observed in studies highlighting that technology acceptance is determined not only by objective capabilities but also by employees' confidence in their competence and organizational support [ 13 , 33 ]. Despite the overall positive disposition, the least favorable rating was given to practical experience in using AI. This highlights a gap between theoretical understanding of AI benefits and actual integration into work processes. Similar insights are presented in the work of other authors, who note that real AI integration is often limited to individual solutions and pilot projects, while systemic implementation is progressing slowly [ 34 , 35 ]. These results suggest that a positive attitude toward technology alone is not sufficient to achieve sustainable integration. Structured training, clear boundaries of responsibility, and practical implementation guidelines are needed to meet employees’ needs and reduce resistance to change. The study results revealed significant differences in attitudes among professional groups, age groups, and employees with varying lengths of work experience. These differences confirm that the acceptance of artificial intelligence is not a homogeneous phenomenon and depends on individual, professional, and contextual factors. The analysis by professional groups showed that physicians were more likely than nurses or other specialists to view the benefits of artificial intelligence positively and had more practical experience applying it. This tendency can be explained by the fact that physicians more frequently encounter clinical decision support systems, diagnostic algorithms, or electronic prescriptions that rely on AI technologies. Moreover, physicians are more likely to participate in training on digital innovations, and their professional status creates more favorable conditions for early access to innovations [ 36 ]. In contrast, nurses more frequently expressed concern about professional autonomy and identity, which is consistent with other research data—AI is sometimes perceived as a threat to the human connection with patients, holistic care, or individual decision-making [ 37 ]. This indicates that successful AI integration requires not only training on technology use but also strengthening nurses’ professional identity and reflecting on their role in a digitizing healthcare system. The analysis of age and work experience revealed clear generational boundaries—younger specialists (under 40 years) more often stated that they were ready to use AI, had positive experiences, and appreciated the potential of these technologies. Meanwhile, employees aged over 50 were more likely to emphasize the threat to professional autonomy and felt less prepared to work with innovations. These results are supported by the Technology Acceptance Model (TAM) and the Diffusion of Innovations theory. According to the TAM model [ 38 ], an employee’s attitude toward technology depends on perceived usefulness and ease of use. The study results show that younger respondents are more likely to perceive AI as beneficial and intuitively understandable, while older ones tend to see risks, uncertainties, or barriers. In the Diffusion of Innovations theory [ 39 ], it is emphasized that the spread of innovations begins with “innovators” and “early adopters,” who are typically younger, more educated, and more open to change—this fully corresponds with the findings of this study. Applying these models helps to better understand why simply offering technological solutions is not enough—it is important to consider employees’ self-confidence, professional values, and opportunities to participate in decision-making and implementation processes. Although the study participants expressed a positive attitude toward the potential of artificial intelligence (AI), their responses also revealed a significant lack of organizational support and formalized training. This poses a risk that even employees who are favorable toward technology may feel distrust, uncertainty, or even resist practical AI integration. Organizational support and training are among the most important factors encouraging the adoption of new technologies. According to recent studies, employees who receive structured training and feel that management supports digital changes are more confident in AI solutions, integrate them into their work faster, and are better able to resolve ethical or practical dilemmas related to technology [ 40 , 41 ]. The study data also confirmed this trend: respondents who stated that they had more practical experience or clear access to learning resources rated the benefits of AI more positively and felt more prepared to use it. In contrast, a lack of organizational support was reflected in lower scores on the factor related to access to training, resources, and management support. This result suggests that in many primary healthcare institutions, the implementation of artificial intelligence is still part of isolated initiatives rather than a systemic change. This is especially relevant given that employees of different professional status and experience may have very different expectations and needs regarding technologies. In addition to organizational resources, the ethical discourse is extremely important. The study found that ethical aspects—such as the distribution of responsibility, transparency of decisions, and the importance of patient consent—raised the most questions, especially among older or more experienced professionals. These results are consistent with insights discussed in the literature review, indicating that AI technologies can be perceived as threatening professional decision-making sovereignty, reducing personal contact with the patient, or complicating the attribution of responsibility [ 42 , 43 ]. Moreover, ethical dilemmas often become a key barrier preventing the integration of technology, even if it functions smoothly on a technical level. Therefore, the implementation of AI should be inseparable from ethical consultations, the development of guidelines, and interdisciplinary discussions to avoid passive resistance or loss of trust. One of the strengths of this study is its large sample and contextual precision. The study included 678 employees from 43 different primary healthcare institutions, allowing the conclusion that the results are sufficiently representative and reflect the situation in various institutions. Another strong aspect is methodological consistency: an original questionnaire was developed for the study, its structure was based on current scientific sources, factor and reliability analyses were performed, and modern statistical tools were applied. The limitations of the study are determined by several factors. First, data were collected using a self-assessment questionnaire, which may be influenced by social desirability bias—respondents may have chosen more “acceptable” answers. Second, the study did not analyze specific AI application situations—it was not assessed whether respondents had in mind electronic prescription systems, diagnostic algorithms, or personnel management solutions. Third, the study was descriptive and correlational, so no causal conclusions can be drawn about the relationships between variables. Also, no interactive group discussions or qualitative analyses were conducted, which could have enriched the results with interpretative data. Based on the study results, several practical recommendations can be formulated for healthcare institutions: Develop consistent AI integration strategies that include not only technological solutions but also staff training, communication, and ethical guidelines. Differentiate training according to employees’ professional groups, age, and experience—technical sessions may suit younger staff, while ethical discussions or practical simulations may be more appropriate for older staff. Involve employees in decision-making regarding AI implementation to ensure trust, transparency, and better adaptation to changes. Promote interdisciplinary dialogue on ethical aspects, especially in cases where AI solutions alter professional responsibilities or the nature of patient-specialist relationships. Further research could deepen the understanding of employee experiences in applying specific AI tools (e.g., clinical decision support systems, triage algorithms, automated documentation) and include qualitative methodology to analyze emotional, value-based, or cultural aspects of attitudes. In addition, it would be appropriate to evaluate the impact of AI on healthcare outcomes, patient satisfaction, and work quality, integrating the perspectives of patients and management. 5. Conclusions Employees of primary healthcare institutions generally view the integration of artificial intelligence (AI) into professional activities positively, especially emphasizing its potential to improve decision-making, increase service efficiency, and accessibility. The strongest support among employees was observed in factors related to perceived AI benefits and personal readiness to use AI; however, evaluations of actual practical experience remained low – this indicates an existing gap between theoretical support and real practical integration. Significant differences were found between professional groups and demographic characteristics: younger and less experienced employees are more likely to trust AI technologies and are more prepared to apply them. In contrast, older or more experienced professionals more frequently express concerns about professional autonomy and ethical challenges. Organizational support and access to training are essential factors for the successful integration of AI. Employees with more learning opportunities or experience evaluate the application of AI significantly more positively. This highlights the need to strengthen leadership involvement, staff preparation, and infrastructure accessibility. Ethical aspects – decision-making responsibility, the changing role of humans, and patient privacy – remain among the main barriers preventing more active use of AI in healthcare. Therefore, it is necessary to implement clear ethical guidelines and involve professionals in their development. Declarations Ethics approval and consent to participate: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee) of SMK University of Applied Science (Protocol No. 86, 19/12/2023). Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper. Consent for publication: Not Applicable. Conflicts of Interest: The authors declare no conflicts of interest. Funding: This research received no external funding. Author Contribution V.R. Conceptualization, Data Curation, Methodology, Writing – Original Draft Preparation, . I. Š. Formal Analysis, Writing – Review & Editing. Acknowledgments: During the preparation of this manuscript/study, the author used Chatgpt for the purposes of English editing. The author have reviewed and edited the output and take full responsibility for the content of this publication. 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MIS Q 13(3):319–340. https://doi.org/10.2307/249008 Rogers EM (2003) Diffusion of innovations, 5th edn. Free Delaney BC, Curcin V, Andreasson A, Arvanitis TN, Carinci F (2024) Trustworthy artificial intelligence for primary care: A call for action. BMJ Health Care Inf 31(1):e100735. https://doi.org/10.1136/bmjhci-2023-100735 Kwok H, Hildt E, Stahl BC (2023) Explainability and transparency in healthcare AI: Balancing innovation and responsibility. AI Soc 38(1):211–225. https://doi.org/10.1007/s00146-022-01415-w Martin G, Jackson T, Chen AH (2023) Integration of artificial intelligence into primary care practice: Lessons learned and future directions. NPJ Digit Med 6(1):92. https://doi.org/10.1038/s41746-023-00876-w Lee MJ, Lee H, Choi B (2024) Factors influencing healthcare workers' attitudes toward artificial intelligence: A systematic review. Nurs Outlook 72(2):101984. https://doi.org/10.1016/j.outlook.2023.101984 Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eIn recent years, the implementation of artificial intelligence (AI) technologies in medicine has been gaining increasing momentum, especially in processes such as clinical decision-making, patient data analysis, application of preventive measures, and healthcare organization. AI solutions are not only a technological innovation\u0026mdash;they change professional roles, communication models, and the everyday reality of clinical practice. Although most studies focus on secondary and tertiary level healthcare institutions, primary healthcare is increasingly becoming a field for AI application experiments, especially aiming for efficiency, personalization, and accessibility of services.\u003c/p\u003e\u003cp\u003eAt the same time, many questions arise regarding the practical application of AI in real clinical settings\u0026mdash;employee preparedness, organizational support, ethical dilemmas, and trust in new technologies. Empirical studies show that the acceptance of technology depends not only on its functionality but also on employees' experience, professional identity, and institutional conditions. Despite growing interest, systematic knowledge about employees\u0026rsquo; attitudes toward AI in the context of primary healthcare is still lacking. This is especially relevant in the context of smaller countries, where healthcare resources are limited.\u003c/p\u003e\u003cp\u003eIn this context, this study aims to assess the experience, attitudes, and perceptions of primary healthcare workers regarding the application of AI in their professional practice. The objective of the study is to analyze what factors determine a favorable or cautious attitude toward AI, what practical and ethical barriers arise, and what organizational solutions are needed for effective and responsible AI integration.\u003c/p\u003e\u003cp\u003eThis study is based on the latest international scientific sources, quantitative methodology, and includes a wide range of respondents from various primary healthcare institutions. It is expected that the results of the study will contribute to better AI implementation policies, organizational decisions, and training planning in the primary healthcare sector.\u003c/p\u003e\u003cp\u003eThe development of artificial intelligence (AI) technologies is changing the operating models of the primary healthcare sector, especially in areas where decision-making, information analysis, and patient data management are required. In recent years, AI tools have been integrated not only at the secondary or tertiary level but increasingly in primary care, where healthcare professionals have direct and continuous contact with patients. AI\u0026rsquo;s potential is manifested in various forms\u0026mdash;from symptom assessment algorithms and electronic decision support tools to natural language recognition systems that allow for the automation of documentation processes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOne of the most important factors determining the successful integration of AI in primary healthcare is the readiness of employees to accept and apply these technologies. Studies show that primary care physicians, nurses, and other professionals often experience both enthusiasm for the possible assistance in daily practice and anxiety related to potential changes in professional roles, automation of work tasks, or redistribution of responsibilities [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition, primary healthcare often faces limited human and technological resources, which may lead to unequal access to AI solutions and their effective use. According to recent research, the digital divide between healthcare institutions became even more pronounced after the COVID-19 pandemic, which accelerated the development of digital solutions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, this development has not always been accompanied by appropriate infrastructure development or staff training.\u003c/p\u003e\u003cp\u003eAI also changes the nature of the doctor\u0026ndash;patient relationship. While algorithms can help in more accurate diagnosis and chronic disease management, there is growing debate about the importance of maintaining human connection and the potential loss of empathy when some functions are transferred to automated systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This aspect is particularly relevant in primary healthcare, where patients often expect not only clinical decisions but also emotional support and a long-term relationship with the service provider.\u003c/p\u003e\u003cp\u003eHealthcare workers' direct experience with AI systems in primary care is a crucial factor influencing the acceptance and long-term application of technology. Although AI is often presented as an advanced solution that can improve patient care and reduce specialist workload, the actual experience of workers is often mixed, involving a lack of trust, learning difficulties, and changes in work routines [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEmpirical studies show that most family doctors and nurses consider AI a useful tool, but they express concerns about the reliability of the technology and its ability to adequately assess complex clinical contexts. AI decision-making systems are often perceived as \u0026ldquo;black boxes\u0026rdquo; whose logic is not fully understood even by experienced professionals [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This raises doubts about whether AI recommendations can be trusted, especially when decisions must be made quickly and based on human judgment and intuition.\u003c/p\u003e\u003cp\u003eMoreover, some employees report that the implementation of AI has increased not only technological but also psychological pressure. Additional responsibilities have emerged\u0026mdash;learning to use new systems, documenting data, and aligning AI recommendations with their clinical judgment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This burden is especially felt in smaller institutions where workers lack technical support or training.\u003c/p\u003e\u003cp\u003eOn the other hand, studies show that when AI integration is carried out consistently and with clear employee involvement, it can greatly facilitate clinical decisions. For example, nurses positively evaluate AI-based reminders regarding preventive measures or patient monitoring, especially in chronic disease management [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These functions not only improve work organization but also help ensure higher quality care.\u003c/p\u003e\u003cp\u003eIt is also observed that employees' attitudes depend on previous experience with technologies\u0026mdash;those who have previously been involved in the implementation of digital tools or have been trained to use information systems evaluate the benefits of AI more positively and adapt more quickly. In contrast, less technologically prepared specialists tend to experience greater resistance, fatigue, and even technological stress [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIt can be stated that employee experiences are heterogeneous and depend on organizational readiness, quality of training, workload, personal attitudes, and the transparency of the technology. This highlights the need to evaluate not only the functioning of AI systems themselves but also the social and professional context in which they are implemented.\u003c/p\u003e\u003cp\u003eHealthcare workers' attitudes toward artificial intelligence in primary healthcare encompass a wide spectrum of emotional, ethical, and professional evaluations. While some professionals view AI as an advanced and useful solution, others associate it with a reduction in professional autonomy, ethical challenges, and anxiety about their future role [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA positive attitude is usually associated with AI's ability to improve work efficiency and the quality of patient care. Professionals value the possibility to use AI for analyzing large amounts of data, predicting disease progression, managing patient flows, or identifying risk factors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Studies show that family doctors and nurses with experience using AI-based clinical decision support tools often mention reduced administrative burden and improved management of patient flows as key benefits [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, professional anxiety often coexists with optimism. One of the most frequently expressed concerns is the fear that AI will eventually replace human decision-making roles, and specialists will become mere \u0026ldquo;executors of technology\u0026rdquo; [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This is especially felt among older employees or those lacking adequate digital competence.\u003c/p\u003e\u003cp\u003eEthical aspects also raise concerns\u0026mdash;employees often question whether AI decisions consider human individuality, context, and values. Automated decisions, despite their accuracy, may be unacceptable when dealing with complex or emotionally sensitive cases [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Especially in primary healthcare, where a long-term relationship with the patient, trust, and confidentiality are important, employees raise questions about ensuring data security and privacy.\u003c/p\u003e\u003cp\u003eIt is important to note that employee attitudes toward AI also depend on how the technology is introduced at the organizational level. When implementation involves employees, explains goals, and applies individualized training, the attitude is much more favorable. Conversely, when AI is presented as superior to human judgment or introduced without consultation, it causes resistance, distrust, and a technological gap [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe overall attitude of employees toward AI reflects a constant tension between the potential of innovation and the preservation of professional identity. Although most acknowledge that AI can improve care quality and support decision-making, it is essential to ensure that technologies are applied ethically, transparently, and with human care at the center. Therefore, employee involvement in technology planning, ethical decision-making, and continuous professional education become key aspects of successful integration.\u003c/p\u003e\u003cp\u003eThe implementation of artificial intelligence in healthcare inevitably raises ethical questions, which become particularly relevant in the primary care sector. Here, employees most often face complex decisions requiring not only clinical competence but also ethical sensitivity, empathy, and a personalized approach to the patient. Artificial intelligence, operating based on algorithms and standardized models, risks oversimplifying decision-making, disregarding the unique situation of the patient [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOne of the key ethical challenges is the distribution of responsibility. When decisions are made based on AI recommendations, healthcare professionals face the question of who is responsible for an inappropriate decision\u0026mdash;the human or the system? [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This uncertainty reduces trust in technology and hinders its application in practice. In addition, there are questions about information transparency\u0026mdash;often the justification of AI decisions is limited, which contradicts the principles of informed decision-making and patient autonomy.\u003c/p\u003e\u003cp\u003eAnother important aspect is data privacy and security. AI systems often rely on large amounts of patient data, which raises the risk of misuse of this data, especially if the technologies are implemented without adequate infrastructure or legal regulation. Employees, especially in nursing and family medicine, express concern about potential data leaks and loss of patient trust [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOrganizational culture and attitude toward innovation also determine how successfully AI is accepted. Research shows that organizations investing in employee involvement, leadership, and open communication about changes can ensure more positive technology adoption [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In contrast, hierarchically rigid or resource-limited organizations often face resistance, cynicism, and lack of AI system implementation in daily activities.\u003c/p\u003e\u003cp\u003eLearning and preparedness aspects are crucial\u0026mdash;without adequate education, employees do not feel competent to use AI solutions. Lacking time, resources, or individualized training, technology adoption becomes a formal and ineffective process. In addition, practical training is often lacking, which would allow understanding not only the technical operation but also the situations in which AI can or should not be applied [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eVarious studies emphasize that consistent, ongoing, and contextualized training must be an integral part of AI implementation. This is especially relevant in primary healthcare, where employees often work independently, without IT specialists or technology consultants. The ability not only to use the system but also to critically assess its recommendations is an essential element of safe and ethical AI use.\u003c/p\u003e\u003cp\u003eIn summary, the implementation of AI in primary healthcare cannot be successful if ethical, organizational, and educational components are ignored. Only by aligning technological solutions with the principles of human dignity, safety, responsibility, and professional identity can sustainable and socially acceptable progress be achieved.\u003c/p\u003e\u003cp\u003eThe conducted literature analysis reveals that the integration of artificial intelligence (AI) into primary healthcare is a complex process requiring not only technological solutions but also social, organizational, and ethical preparedness. Although many studies on AI applications in healthcare have been conducted in recent years, many aspects remain unexplored in the context of primary care, especially related to employee experiences and attitudes.\u003c/p\u003e\u003cp\u003eFirst, the analyzed sources show that healthcare workers have mixed feelings about AI\u0026mdash;some see it as an opportunity to improve work efficiency and patient care, while others see it as a threat to professional autonomy and the relationship with the patient. This attitude often depends on digital literacy, previous experience, and organizational support. However, many studies still describe employee attitudes superficially, relying only on general surveys or conceptual evaluations, rather than in-depth analyses or contextualized experiences [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecond, employees' experiences using AI\u0026mdash;especially in the long term\u0026mdash;remain poorly documented. Most scientific works analyze the initial implementation of AI or pilot projects, but there is a lack of empirical material on long-term changes in work processes, professional identity, and models of patient care. Moreover, little attention is given to how AI integration affects team dynamics, distribution of responsibilities, and interprofessional collaboration [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThird, organizational and learning aspects are often cited as major challenges, but this area also lacks consistent data. There is no unified approach to which training models would be most effective, how employees assess the quality of training, or how institution leaders contribute to technological transformation. Some studies emphasize that successful AI integration requires a culturally prepared organization, but specific strategies are often missing [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFourth, ethical aspects\u0026mdash;such as responsibility, decision transparency, patient autonomy, and data privacy\u0026mdash;are mostly discussed at a theoretical level. There is a lack of practical research revealing how these issues are addressed in real primary care practice, what mechanisms are in place, and what changes employees expect or fear. Moreover, the transparency of AI decisions (the \u0026ldquo;black box\u0026rdquo; problem) still raises distrust, which reduces the extent of their application [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFinally, it is noted that most studies focus on doctors\u0026rsquo; experiences, while the perspectives of nurses, nurse assistants, or other team members are analyzed much less frequently. Considering that primary healthcare services are provided by multiprofessional teams, it is necessary to broaden the research field to include employees at all levels and assess their interactions with AI solutions.\u003c/p\u003e\u003cp\u003eConsidering these gaps, it can be stated that a new, empirically based study is needed, focused on the experiences and attitudes of employees working in primary healthcare toward AI. Such a study would help not only to understand practical challenges and expectations but also to form the foundation for sustainable technology implementation strategies that meet employee needs.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThis study employed a quantitative cross-sectional research design, which allows for the simultaneous assessment of participants\u0026rsquo; attitudes, experiences, and demographic characteristics. The cross-sectional design is particularly suitable when aiming to describe the distribution of opinions in a large population and examine their associations with certain factors [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The aim of the study was to explore the attitudes and experiences of employees working in primary healthcare institutions related to the application of artificial intelligence (AI) in professional practice.\u003c/p\u003e\u003cp\u003eThe study sample consisted of 678 employees from 43 primary healthcare institutions in Lithuania, selected using purposive sampling. This method is recommended when it is necessary to include a specific professional group with direct experience of the phenomenon under investigation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Institutions were selected based on geographic diversity, the nature of their activities, and the volume of patients served. All research activities were carried out only after obtaining official written consent from the management of the institutions.\u003c/p\u003e\u003cp\u003eInclusion criteria was the employee works in a primary healthcare institution (physician, nurse, or other healthcare personnel) and a signed informed consent document.\u003c/p\u003e\u003cp\u003eTo ensure the appropriateness of the questionnaire, a pilot study was conducted in one institution, involving 32 employees. Pilot studies help assess the clarity, comprehensibility, and structure of the instruments and allow for content adjustments before final implementation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Based on participants\u0026rsquo; feedback, some statements were refined or removed to improve internal consistency and content validity.\u003c/p\u003e\u003cp\u003eData were collected using a structured paper-based questionnaire, completed anonymously at employees\u0026rsquo; workplaces. The questionnaire consisted of 30 statements evaluated on a 5-point Likert scale (from \u0026ldquo;strongly disagree\u0026rdquo; to \u0026ldquo;strongly agree\u0026rdquo;) and 4 demographic questions (gender, age group, position, and work experience). This format is considered appropriate for attitude research as it allows for the quantitative assessment of the strength of attitudes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The full English version of the questionnaire used in this study is available as Supplementary File 1.\u003c/p\u003e\u003cp\u003eThe questionnaire was developed based on the latest scientific research on AI applications in healthcare [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe internal consistency of the questionnaire was assessed using the Cronbach\u0026rsquo;s alpha coefficient. This indicator reflects how reliably the statements measure the same construct\u0026mdash;in this case, employees\u0026rsquo; attitudes toward the application of artificial intelligence in healthcare. A separate alpha coefficient was calculated for each thematic block, and an overall value was provided for the entire questionnaire (see 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\u003eCronbach\u0026rsquo;s alpha coefficients by thematic blocks.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThematic block\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCronbach\u0026rsquo;s alpha\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived benefits of artificial intelligence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety about professional autonomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsage experience and learning opportunities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthical and responsibility aspects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganizational support and technological readiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonal readiness to use AI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall questionnaire reliability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89\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\u003eAll Cronbach\u0026rsquo;s alpha results exceed the 0.76 threshold, indicating very good internal consistency of the questionnaire. The highest reliability was demonstrated by the block \u0026ldquo;Usage experience and learning opportunities\u0026rdquo; (0.84), and the overall questionnaire coefficient (0.89) confirms that the research instrument was suitable and reliable for measuring employee attitudes.\u003c/p\u003e\u003cp\u003e The study was conducted in accordance with the principles of the World Medical Association\u0026rsquo;s Declaration of Helsinki (2013). The study plan was reviewed by a research and ethics committee (following the SMK example), and the research was carried out only after receiving permission. All participants signed an informed consent form that explained the study's purpose, the voluntary nature of participation, and the assurance of data confidentiality and anonymity.\u003c/p\u003e\u003cp\u003eThe main principles of bioethics were followed [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]: non-maleficence \u0026amp; beneficence \u0026ndash; the impact on participants was minimal, with no harm; voluntariness and informed consent \u0026ndash; participation was voluntary; privacy and confidentiality \u0026ndash; no personal data were collected; Justice \u0026ndash; equal participation conditions were provided for all employees; Transparency \u0026ndash; the goals and process of the study were clearly presented.\u003c/p\u003e\u003cp\u003eStatistical analysis was performed using IBM SPSS Statistics software (version 29). The following analyses were applied: Descriptive statistics \u0026ndash; mean, median, standard deviation, minimum, and maximum values were calculated; Correlation analysis \u0026ndash; Pearson correlation coefficient was used to assess relationships between variables; Reliability \u0026ndash; internal consistency was evaluated using Cronbach\u0026rsquo;s alpha; values above 0.70 were considered acceptable [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]; Factor analysis \u0026ndash; exploratory factor analysis (EFA) was performed to identify the main dimensions of the questionnaire. KMO and Bartlett\u0026rsquo;s test were used to assess the suitability of the structure; Non-parametric tests \u0026ndash; Kruskal\u0026ndash;Wallis and Mann\u0026ndash;Whitney U tests were applied when data did not meet normality assumptions; Parametric tests \u0026ndash; ANOVA and independent samples t-test were used to evaluate group differences. Statistical significance was considered when p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 678 employees working in primary healthcare institutions participated in the study. The respondents represented various professions, age groups, and levels of work experience. This diverse participant profile allows for a comprehensive assessment of healthcare workers\u0026rsquo; attitudes from different perspectives.\u003c/p\u003e\u003cp\u003eThe composition of the sample shows that most participants were women (82.4%) and nurses (54.3%). More than one-third of respondents indicated having over 20 years of work experience, and the dominant age group was 41\u0026ndash;50 years. This suggests that the responses reflect the opinions of experienced professionals.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of respondents\u0026rsquo; attitudes toward artificial intelligence.\u003c/b\u003e Respondents evaluated 30 statements related to the application of artificial intelligence in primary healthcare. A five-point Likert scale was used for the evaluation (1 \u0026ndash; strongly disagree, 5 \u0026ndash; strongly agree). The statements were divided into six thematic blocks based on their content. Means and standard deviations for each block were calculated (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eMeans and standard deviations of thematic blocks of respondents\u0026rsquo; attitudes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThematic block\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard deviation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived benefits of artificial intelligence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety about professional autonomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsage experience and learning opportunities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthical and responsibility aspects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganizational support and technological readiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonal readiness to use AI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.59\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\u003eThe highest mean was recorded in the thematic block \u0026ldquo;Perceived benefits of artificial intelligence\u0026rdquo; (mean\u0026thinsp;=\u0026thinsp;4.12), indicating that most employees recognize the potential of this technology to improve healthcare services. Personal readiness to use AI was also evaluated positively (mean\u0026thinsp;=\u0026thinsp;4.06). Meanwhile, the lowest mean was in \u0026ldquo;Usage experience and learning opportunities\u0026rdquo; (mean\u0026thinsp;=\u0026thinsp;3.38), reflecting the need for better preparation and practical training among employees.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExploratory factor analysis.\u003c/b\u003e To confirm the construct validity of the questionnaire and identify the main dimensions of employees\u0026rsquo; attitudes, an exploratory factor analysis was conducted using the principal components method with Varimax rotation. Prior to that, the data suitability for factor analysis was assessed. The Kaiser\u0026ndash;Meyer\u0026ndash;Olkin measure (KMO)\u0026thinsp;=\u0026thinsp;0.902 indicated very good sample adequacy. Bartlett\u0026rsquo;s test of sphericity: χ\u0026sup2; = 6423.51; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 confirmed that the correlation matrix significantly differs from the identity matrix and is suitable for factor analysis.\u003c/p\u003e\u003cp\u003eThe analysis revealed six significant factor groups that together explained 71.2 percent of the total data variance. Each factor was interpreted and named based on the statements with the highest loadings (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactor names and explained variance.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExplained variance (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePerceived benefits of artificial intelligence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThreat to professional autonomy and identity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePersonal readiness and motivation to use AI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrganizational support and learning opportunities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEthical aspects and perception of responsibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePractical experience of using AI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal explained variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.2\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\u003eThe factor analysis confirmed the theoretical basis of the questionnaire and identified six semantically clear factor groups:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePerceived benefits of artificial intelligence \u0026ndash; includes statements about AI\u0026rsquo;s ability to improve work efficiency, reduce error risk, and increase patient accessibility.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThreat to professional autonomy and identity \u0026ndash; reflects employees\u0026rsquo; concerns about the potential impact of technology on their professional roles, decision-making, and maintaining human connection with patients.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePersonal readiness and motivation to use AI \u0026ndash; relates to individual willingness, curiosity, and openness to applying AI in daily practice.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eOrganizational support and learning opportunities \u0026ndash; include evaluations of management communication, availability of technical support, and sufficiency of training.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEthical aspects and perception of responsibility \u0026ndash; reveals employees\u0026rsquo; views on decision transparency, data protection, and legal uncertainty.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePractical experience of using AI \u0026ndash; includes frequency of actual use, integration of tools into work processes, and trust in their functionality.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe analysis demonstrated a good internal structure of the questionnaire, confirmed the theoretical components of attitudes, and provides a basis for further statistical analysis. The six factors highlight the key aspects influencing the acceptance of artificial intelligence in primary healthcare.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of attitude differences by demographic variables.\u003c/b\u003e The study aimed to determine whether employees\u0026rsquo; attitudes toward artificial intelligence significantly differed based on their gender, position, age group, and work experience. The data were tested for normality, and depending on the results, t-tests, ANOVA, or the Kruskal\u0026ndash;Walli\u0026rsquo;s test were applied.\u003c/p\u003e\u003cp\u003eWhen comparing the means of males and females across each attitude factor, significant differences were identified in only two: Personal readiness and motivation to use AI and Ethical aspects and perception of responsibility. Male respondents scored slightly higher in these areas (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAttitude differences by gender.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (Men)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean (Women)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived AI benefits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThreat to professional autonomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonal readiness and motivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganizational support and training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthical aspects and responsibility perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePractical AI usage experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.351\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\u003eMen's attitudes regarding personal readiness to use AI and their evaluations of ethical aspects were statistically significantly more favorable than women's (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), although the differences were not large.\u003c/p\u003e\u003cp\u003eStatistically significant differences were found among different professional groups\u0026mdash;doctors, nurses, and other specialists\u0026mdash;across many aspects of attitudes. Doctors rated AI benefits and its practical applicability higher, while nurses more often expressed concern about professional autonomy (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAttitude differences by professional position.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived AI benefits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThreat to professional autonomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonal readiness and motivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganizational support and training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthical aspects and responsibility perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePractical AI usage experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant\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\u003eDoctors were more likely to evaluate AI positively and had more experience with these tools. Nurses expressed greater concern regarding the impact of AI on their professional identity and decision-making.\u003c/p\u003e\u003cp\u003eRespondents were divided into five age groups: under 30, 31\u0026ndash;40, 41\u0026ndash;50, 51\u0026ndash;60, and 61 and over. One-way ANOVA was used to analyze differences in attitudes toward AI across these groups (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAttitude differences by age group.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived AI benefits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThreat to professional autonomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonal readiness and motivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganizational support and training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthical aspects and responsibility perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePractical AI usage experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant\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\u003eStatistically significant differences by age were found in four out of six factors. Older employees (aged 51 and over) more often expressed concern about professional autonomy, while younger respondents (under 40) showed greater personal openness and motivation to use artificial intelligence. Younger employees also more frequently reported having at least minimal practical experience with AI tools.\u003c/p\u003e\u003cp\u003eRespondents\u0026rsquo; work experience was categorized into four groups: up to 5 years, 6\u0026ndash;10 years, 11\u0026ndash;20 years, and more than 20 years. One-way ANOVA was applied to assess differences in attitudes toward artificial intelligence between these groups (see Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAttitude differences by work experience.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived AI benefits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThreat to professional autonomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonal readiness and motivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganizational support and training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthical aspects and responsibility perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePractical AI usage experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant\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\u003eWork experience duration was statistically significantly related to several aspects of attitudes. Most of the differences were identified between professionals with less than 10 years and those with more than 20 years of experience. Employees with longer work experience more often expressed concern about changes in professional roles and were more sensitive to the lack of organizational support. Meanwhile, younger colleagues were more likely to evaluate AI benefits positively and more frequently reported real-world use cases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation analysis.\u003c/b\u003e To determine the relationships between demographic variables (age, work experience) and employees\u0026rsquo; attitudes toward artificial intelligence, Spearman\u0026rsquo;s correlation coefficients were calculated. The analysis also included attitude factors identified during factor analysis (see Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpearman correlations between attitude factors and demographic variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAttitude Factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWork experience\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGender (1 \u0026ndash; male, 2 \u0026ndash; female)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived AI benefits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.18**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.16**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThreat to professional autonomy and identity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.21**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonal readiness and motivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.22**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.19**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.11*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganizational support and learning opportunities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.14*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.12*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthical aspects and responsibility perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.10*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePractical AI usage experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.25**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.21**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.02\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\u003eThe correlation analysis revealed significant but weak to moderate associations:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eAge and work experience were statistically significantly negatively correlated with perceived AI benefits, personal readiness, and practical experience, and positively with the threat to professional autonomy.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGender showed a small but significant correlation only with personal readiness (lower means among women) and evaluation of ethical aspects (women were more concerned about these issues).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe analysis of the study data revealed several key trends related to primary healthcare workers\u0026rsquo; attitudes toward the application of artificial intelligence (AI) in professional practice. Evaluating 30 statements on a Likert scale and conducting factor analysis revealed that employee attitudes are multidimensional and depend on various personal and organizational factors.\u003c/p\u003e\u003cp\u003eOverall, the highest means were found in the factors related to perceived AI benefits and personal willingness to use these technologies. These results suggest that study participants generally recognize the potential of AI to improve the quality, efficiency, and accessibility of healthcare services. In contrast, the lowest-rated aspect\u0026mdash;practical AI usage experience\u0026mdash;reveals a gap between theoretical favorability and the actual integration of these technologies into daily practice.\u003c/p\u003e\u003cp\u003eThe suitability of the questionnaire\u0026rsquo;s construct was confirmed by high reliability indicators. The overall Cronbach\u0026rsquo;s alpha coefficient was 0.89, and the individual factors exceeded the 0.76 threshold, indicating very good internal consistency. The exploratory factor analysis identified six conceptually grounded factors: (1) perceived AI benefits, (2) threat to professional autonomy and identity, (3) personal readiness and motivation, (4) organizational support and learning opportunities, (5) ethical aspects and responsibility perception, and (6) practical AI usage experience. These six factors explained 71.2% of the total variance, indicating high construct validity.\u003c/p\u003e\u003cp\u003eBetween-group differences revealed important demographic influences. Statistically significant differences were identified by position, age, and work experience. Doctors were more likely than other specialists to emphasize AI benefits and practical applicability, while nurses more often expressed concern about professional autonomy. Younger and less experienced respondents demonstrated greater openness to innovation, higher confidence in technologies, and more positive evaluations. Older and more experienced professionals more often indicated ethical and professional concerns.\u003c/p\u003e\u003cp\u003eThe correlation analysis additionally confirmed these trends: age and work experience were significantly negatively correlated with a positive attitude toward AI benefits, readiness, and practical use, and positively with perceived threat to professional autonomy. Gender-related correlations were weak but statistically significant in aspects related to ethical dilemmas and individual readiness to use AI.\u003c/p\u003e\u003cp\u003e In summary, the study results show that primary healthcare workers are generally positively inclined toward AI; however, successful integration requires stronger organizational support, targeted training, and ethically clear implementation principles. Attitude differences between professional groups and demographic segments suggest that AI implementation processes must include individualized interventions, considering employees\u0026rsquo; experience, age, and professional identity.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe aim of this study was to analyze the experience and attitudes of employees in primary healthcare institutions regarding the application of artificial intelligence (AI) in the healthcare context. The results obtained confirm a trend described in previous scientific studies\u0026mdash;most healthcare professionals acknowledge the potential of AI, but its integration still faces practical, psychological, and organizational barriers.\u003c/p\u003e\u003cp\u003eAccording to the study data, the highest-rated factor by employees was the perceived benefits of AI, especially in aspects such as error reduction, support in clinical decision-making, and improved patient accessibility. This aligns with current scientific literature\u0026mdash;various studies emphasize that AI technologies can contribute to more efficient service delivery, particularly in the areas of decision support systems and patient queue management [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnother highly rated factor\u0026mdash;personal readiness and motivation to use AI\u0026mdash;suggests that employees are open to innovation; however, a clear system and supportive environment are required. This trend has also been observed in studies highlighting that technology acceptance is determined not only by objective capabilities but also by employees' confidence in their competence and organizational support [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the overall positive disposition, the least favorable rating was given to practical experience in using AI. This highlights a gap between theoretical understanding of AI benefits and actual integration into work processes. Similar insights are presented in the work of other authors, who note that real AI integration is often limited to individual solutions and pilot projects, while systemic implementation is progressing slowly [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese results suggest that a positive attitude toward technology alone is not sufficient to achieve sustainable integration. Structured training, clear boundaries of responsibility, and practical implementation guidelines are needed to meet employees\u0026rsquo; needs and reduce resistance to change.\u003c/p\u003e\u003cp\u003eThe study results revealed significant differences in attitudes among professional groups, age groups, and employees with varying lengths of work experience. These differences confirm that the acceptance of artificial intelligence is not a homogeneous phenomenon and depends on individual, professional, and contextual factors.\u003c/p\u003e\u003cp\u003eThe analysis by professional groups showed that physicians were more likely than nurses or other specialists to view the benefits of artificial intelligence positively and had more practical experience applying it. This tendency can be explained by the fact that physicians more frequently encounter clinical decision support systems, diagnostic algorithms, or electronic prescriptions that rely on AI technologies. Moreover, physicians are more likely to participate in training on digital innovations, and their professional status creates more favorable conditions for early access to innovations [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn contrast, nurses more frequently expressed concern about professional autonomy and identity, which is consistent with other research data\u0026mdash;AI is sometimes perceived as a threat to the human connection with patients, holistic care, or individual decision-making [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This indicates that successful AI integration requires not only training on technology use but also strengthening nurses\u0026rsquo; professional identity and reflecting on their role in a digitizing healthcare system.\u003c/p\u003e\u003cp\u003eThe analysis of age and work experience revealed clear generational boundaries\u0026mdash;younger specialists (under 40 years) more often stated that they were ready to use AI, had positive experiences, and appreciated the potential of these technologies. Meanwhile, employees aged over 50 were more likely to emphasize the threat to professional autonomy and felt less prepared to work with innovations. These results are supported by the Technology Acceptance Model (TAM) and the Diffusion of Innovations theory.\u003c/p\u003e\u003cp\u003eAccording to the TAM model [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], an employee\u0026rsquo;s attitude toward technology depends on perceived usefulness and ease of use. The study results show that younger respondents are more likely to perceive AI as beneficial and intuitively understandable, while older ones tend to see risks, uncertainties, or barriers. In the Diffusion of Innovations theory [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], it is emphasized that the spread of innovations begins with \u0026ldquo;innovators\u0026rdquo; and \u0026ldquo;early adopters,\u0026rdquo; who are typically younger, more educated, and more open to change\u0026mdash;this fully corresponds with the findings of this study.\u003c/p\u003e\u003cp\u003eApplying these models helps to better understand why simply offering technological solutions is not enough\u0026mdash;it is important to consider employees\u0026rsquo; self-confidence, professional values, and opportunities to participate in decision-making and implementation processes.\u003c/p\u003e\u003cp\u003eAlthough the study participants expressed a positive attitude toward the potential of artificial intelligence (AI), their responses also revealed a significant lack of organizational support and formalized training. This poses a risk that even employees who are favorable toward technology may feel distrust, uncertainty, or even resist practical AI integration.\u003c/p\u003e\u003cp\u003eOrganizational support and training are among the most important factors encouraging the adoption of new technologies. According to recent studies, employees who receive structured training and feel that management supports digital changes are more confident in AI solutions, integrate them into their work faster, and are better able to resolve ethical or practical dilemmas related to technology [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The study data also confirmed this trend: respondents who stated that they had more practical experience or clear access to learning resources rated the benefits of AI more positively and felt more prepared to use it.\u003c/p\u003e\u003cp\u003eIn contrast, a lack of organizational support was reflected in lower scores on the factor related to access to training, resources, and management support. This result suggests that in many primary healthcare institutions, the implementation of artificial intelligence is still part of isolated initiatives rather than a systemic change. This is especially relevant given that employees of different professional status and experience may have very different expectations and needs regarding technologies.\u003c/p\u003e\u003cp\u003eIn addition to organizational resources, the ethical discourse is extremely important. The study found that ethical aspects\u0026mdash;such as the distribution of responsibility, transparency of decisions, and the importance of patient consent\u0026mdash;raised the most questions, especially among older or more experienced professionals. These results are consistent with insights discussed in the literature review, indicating that AI technologies can be perceived as threatening professional decision-making sovereignty, reducing personal contact with the patient, or complicating the attribution of responsibility [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, ethical dilemmas often become a key barrier preventing the integration of technology, even if it functions smoothly on a technical level. Therefore, the implementation of AI should be inseparable from ethical consultations, the development of guidelines, and interdisciplinary discussions to avoid passive resistance or loss of trust.\u003c/p\u003e\u003cp\u003eOne of the strengths of this study is its large sample and contextual precision. The study included 678 employees from 43 different primary healthcare institutions, allowing the conclusion that the results are sufficiently representative and reflect the situation in various institutions. Another strong aspect is methodological consistency: an original questionnaire was developed for the study, its structure was based on current scientific sources, factor and reliability analyses were performed, and modern statistical tools were applied.\u003c/p\u003e\u003cp\u003eThe limitations of the study are determined by several factors. First, data were collected using a self-assessment questionnaire, which may be influenced by social desirability bias\u0026mdash;respondents may have chosen more \u0026ldquo;acceptable\u0026rdquo; answers. Second, the study did not analyze specific AI application situations\u0026mdash;it was not assessed whether respondents had in mind electronic prescription systems, diagnostic algorithms, or personnel management solutions. Third, the study was descriptive and correlational, so no causal conclusions can be drawn about the relationships between variables. Also, no interactive group discussions or qualitative analyses were conducted, which could have enriched the results with interpretative data.\u003c/p\u003e\u003cp\u003eBased on the study results, several practical recommendations can be formulated for healthcare institutions:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e Develop consistent AI integration strategies that include not only technological solutions but also staff training, communication, and ethical guidelines.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e Differentiate training according to employees\u0026rsquo; professional groups, age, and experience\u0026mdash;technical sessions may suit younger staff, while ethical discussions or practical simulations may be more appropriate for older staff.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eInvolve employees in decision-making regarding AI implementation to ensure trust, transparency, and better adaptation to changes.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePromote interdisciplinary dialogue on ethical aspects, especially in cases where AI solutions alter professional responsibilities or the nature of patient-specialist relationships.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eFurther research could deepen the understanding of employee experiences in applying specific AI tools (e.g., clinical decision support systems, triage algorithms, automated documentation) and include qualitative methodology to analyze emotional, value-based, or cultural aspects of attitudes. In addition, it would be appropriate to evaluate the impact of AI on healthcare outcomes, patient satisfaction, and work quality, integrating the perspectives of patients and management.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eEmployees of primary healthcare institutions generally view the integration of artificial intelligence (AI) into professional activities positively, especially emphasizing its potential to improve decision-making, increase service efficiency, and accessibility.\u003c/p\u003e\u003cp\u003eThe strongest support among employees was observed in factors related to perceived AI benefits and personal readiness to use AI; however, evaluations of actual practical experience remained low \u0026ndash; this indicates an existing gap between theoretical support and real practical integration.\u003c/p\u003e\u003cp\u003eSignificant differences were found between professional groups and demographic characteristics: younger and less experienced employees are more likely to trust AI technologies and are more prepared to apply them. In contrast, older or more experienced professionals more frequently express concerns about professional autonomy and ethical challenges.\u003c/p\u003e\u003cp\u003eOrganizational support and access to training are essential factors for the successful integration of AI. Employees with more learning opportunities or experience evaluate the application of AI significantly more positively. This highlights the need to strengthen leadership involvement, staff preparation, and infrastructure accessibility.\u003c/p\u003e\u003cp\u003eEthical aspects \u0026ndash; decision-making responsibility, the changing role of humans, and patient privacy \u0026ndash; remain among the main barriers preventing more active use of AI in healthcare. Therefore, it is necessary to implement clear ethical guidelines and involve professionals in their development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate:\u003c/h2\u003e\u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee) of SMK University of Applied Science (Protocol No. 86, 19/12/2023). Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.\u003c/p\u003e\u003ch2\u003eConsent for publication:\u003c/h2\u003e\u003cp\u003eNot Applicable.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eV.R. Conceptualization, Data Curation, Methodology, Writing \u0026ndash; Original Draft Preparation, . I. Š. Formal Analysis, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e\u003cp\u003eDuring the preparation of this manuscript/study, the author used Chatgpt for the purposes of English editing. The author have reviewed and edited the output and take full responsibility for the content of this publication.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eMaterial of this Research can be found at National Open Access Research Data Archive (MIDAS). https://midas.lt:443/action/resources/3b0b7763-7adc-402f-80b0-3adaf0128e82\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi Y, Zhang J, Huang J, Chen L (2023) Natural language processing in primary care: Current applications and future directions. 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Nurs Outlook 72(2):101984. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.outlook.2023.101984\u003c/span\u003e\u003cspan address=\"10.1016/j.outlook.2023.101984\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"artificial intelligence, healthcare, employee attitude, primary care","lastPublishedDoi":"10.21203/rs.3.rs-7346278/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7346278/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground and Objectives:\u003c/em\u003e The integration of artificial intelligence (AI) in healthcare has gained increasing attention to enhance clinical decision-making, service efficiency, and accessibility. While global research highlights both the potential and the challenges of AI implementation, little is known about how primary healthcare professionals perceive and experience this technological shift. This study aimed to examine the attitudes, experiences, and perceived barriers among primary healthcare workers regarding the application of AI in their professional practice. \u003cem\u003eMaterials and Methods:\u003c/em\u003e A cross-sectional survey was conducted among 678 employees from 43 primary healthcare institutions in Lithuania. Data were collected using a structured questionnaire developed based on existing literature and validated through exploratory factor analysis. Six key factors were identified: perceived AI benefits, threat to professional autonomy, personal readiness and motivation, organizational support and training, ethical concerns, and practical experience. Statistical analysis included ANOVA, t-tests, and Spearman correlation to assess differences across demographic and professional groups. \u003cem\u003eResults:\u003c/em\u003eRespondents generally expressed a favorable attitude toward AI, especially recognizing its potential to improve care quality and efficiency. The highest scores were observed in perceived AI benefits and individual readiness. However, practical experience with AI was limited. Significant differences emerged across age, professional role, and years of experience: younger and less experienced staff showed greater enthusiasm and openness to AI, while older and more experienced professionals voiced concerns about autonomy and ethical implications. Organizational support and access to training were positively associated with AI acceptance. \u003cem\u003eConclusions:\u003c/em\u003e Primary healthcare workers are positively inclined toward the adoption of AI but face a gap between perceived potential and actual implementation. Successful integration requires tailored training, strong leadership support, and clear ethical frameworks. Addressing individual, organizational, and ethical factors is critical to fostering trust and enabling sustainable AI use in healthcare settings.\u003c/p\u003e","manuscriptTitle":"Healthcare Workers’ Perspectives on Artificial Intelligence in Primary Care","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 14:02:31","doi":"10.21203/rs.3.rs-7346278/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-31T10:44:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T20:09:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216394687331913651081613471718443911954","date":"2025-10-28T06:58:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T14:20:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329790407148847283764353532575596906047","date":"2025-10-26T11:19:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282569999477331981138663733636725762505","date":"2025-10-16T18:29:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231141892867427834701912405571181893611","date":"2025-10-08T08:09:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165353918189397949065519626284802474489","date":"2025-09-30T16:50:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-15T16:15:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-15T16:04:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-15T12:25:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-06T18:28:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Digital Health","date":"2025-09-06T18:25:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c45c708e-2649-41d8-aea1-425baf765e59","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-01T10:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 14:02:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7346278","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7346278","identity":"rs-7346278","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}