Surgical nurses’ artificial intelligence literacy and readiness levels for medical artificial intelligence

AI is a leading technology that is shaping the transformation of healthcare systems in recent years [1]. Artificial intelligence applications in healthcare help professionals to assess, diagnose, plan treatment, and monitor and forecast patient risk to increase safety and quality of care [2]. AI is making substantial contributions to nursing practice. Systems based on AI will allow nurses to help with monitoring vital signs, analyzing patient data, and early detection of clinically significant patterns [3, 4]. This can improve patient outcomes by allowing for early detection of potential complications [3, 5].

Furthermore, due to the AI’s ability to reduce administrative burden, nurses are able to dedicate more time and effort to patients. This increases the efficacy and quality of care processes [6, 7]. Nursing practice refers to the skilled application of clinical judgment and patient-centred values in the provision of technology-based care. In clinical settings, especially in surgical units focused on rapid decision-making, integrated work with advanced technology and patient safety, the ability to work with artificial intelligence has become an essential component of professional competence.

The infusion of artificial intelligence into surgical decision-making processes improves patient care. With the goal of enhancing the information available in pre- and postoperative environments, AI will be implemented more widely [8, 9]. At the moment, surgical nurses have their physical workload lowered thanks to robots with AI software. A review of the literature indicates that robots with different features can be used in the field of surgical nursing. The Da Vinci robot that is controlled by the surgeon is noted for reducing the burden of operating room nurses and increasing the efficiency of surgery by removing the anatomical limitations of human hands during surgery [10].

Ro-bear is a type of robot care bear resembling a polar bear whose job involves transferring patients to their beds and wheelchairs. The use of the Cody robot with bedridden people can help them maintain personal hygiene, dressing, and rehabilitation for stroke patients. The Pepper robot can help patients accustom themselves to the hospital environment due to its multilingualism. The Nao robot is a robot that offers physical support and motivation in treatments and rehabilitation processes. This robot is especially useful for sensitive groups such as children and elderly individuals. The SAM robots visit patients in their rooms to check their status and perform other basic assessments. The IV Robot RIVA ensures patient safety by preparing medications and perfusions administered by the intravenous route in the right doses [6, 11].

The incorporation of surgical nurses into AI-related applications largely depends on their knowledge, perceptions, and readiness relative to AI-based technologies, despite all these efforts. According to the literature, nurses’ knowledge and application skills regarding their AI literacy level are not yet sufficient [9]. Nonetheless, the great majority of the existing studies were conducted on the general nursing population, and information regarding surgical nurses’ AI literacy and readiness levels for medical AI is rather scarce [7, 9, 12‐15]. Surgical nurses are one of the clinical professionals having maximum interactions with AI-based applications, such as robotic surgical systems, digital patient monitoring tools, automated decision support systems, and sensor-based applications [8, 9]. Thus, it is vital to ascertain the AI awareness and readiness levels in this group for patient safety and for the digitization of clinical decision-making.

The aim of this study is to ascertain the relationship between surgical nurses’ artificial intelligence literacy and their technological readiness. The results are likely to inform revisions to nursing education curricula, approaches for technology integration into clinical practice, and digital health policy-making.

A total of N = 339 nurses participated in the study, and the average age of the nurses was 34.35 ± 9.37 years. 85.5% of the participants were female nurses, and 14.5% were male nurses. In terms of marital status, 67.6% were single and 32.4% were married. When their educational status was examined, it was seen that most of the participants (69.9%) were bachelor’s degree graduates. In terms of distribution according to years of experience in the profession, 40.7% of the participants had 6 years or more of experience. In terms of regional distribution, the vast majority of participants live in Western Turkey (81.4%). As for the settlement unit where they live, 74.9% of participants live in a metropolitan city (Fig. 1). The descriptive characteristics of the participants are presented in Table 1.

The vast majority of participants (83.2%) stated that they had heard of the concepts of AI and robotic nursing before. The percentage of those who believe that AI robots will replace nurses is 15%. 73.2% of participants believe that AI technologies will benefit the nursing profession. A high percentage of respondents (80.5%) believe that AI applications will reduce nurses’ workload. 30.4% of participants reported feeling concerned about their jobs due to the development of AI and robotic technologies. Participants’ views on artificial intelligence are presented in Table 2.

A strong positive correlation was found between the MAIRS score and the total AILS score (rₛ = 0.642, p < 0.001). Similarly, moderate to strong positive and statistically significant relationships were found between the readiness scale and the subdimensions of the Artificial Intelligence Literacy Scale, namely technical understanding (rₛ = 0.606, p < 0.001), critical evaluation (rₛ = 0.672, p < 0.001), and practical application (rₛ = 0.558, p < 0.001) of the Artificial Intelligence Literacy Scale. Additionally, the subscales of the AILS showed strong correlations among themselves (e.g., technical understanding and critical evaluation, rₛ = 0.808, p < 0.001). These findings indicate that both readiness for artificial intelligence and literacy are closely related (Table 3).

Participants’ age was positively correlated with the MAIRS (rₛ=0.314, p < 0.001), the AILS total score (rₛ=0.232, p < 0.001), and its subscales Technical Understanding (rₛ=0.127, p = 0.019) and Practical Application (rₛ=0.281, p < 0.001). Medical Artificial Intelligence Readiness scores differed according to the gender variable (p = 0.018), and the mean scores of men were found to be higher than those of women. In terms of marital status, the scores of single participants on both scales and subscales were found to be significantly higher than those of married participants (p < 0.05). As the level of education increased, a downward trend was observed in Medical Artificial Intelligence Readiness and Artificial Intelligence Literacy scores; bachelor’s degree graduates had the highest averages on both scales (p < 0.05 in all comparisons). No significant difference was found based on years of professional experience (p > 0.05). However, when evaluated in terms of the institution where they worked, nurses working in private hospitals scored higher on both scales than those working in public institutions (p < 0.001) (Table 4).

Table 5 shows that the unit of employment variable also created a significant difference in the AILS and its subscales, with the highest mean scores found in surgical units (p < 0.05). No significant difference was found based on the region of residence (p > 0.05), while in terms of settlement, the average scores of participants living in cities were higher.

Due to the nature of surgical nursing, which is carried out under heavy workloads and time constraints, there is a growing need for AI-supported applications in this field [21, 22]. However, the current literature reveals shortcomings in the integration of AI technologies among nurses actively working in clinical settings [23‐25]. The rapidly increasing interest in AI in the healthcare field necessitates the development of nurses’ abilities to understand, evaluate, and effectively use these systems, given their important role in the surgical team. As a result, the enhancement of nurses’ AI literacy levels will bring about more effective and extensive employment of AI-based applications in nursing care. This study was conducted to evaluate the effect of AI literacy on the level of readiness of surgical nurses.

The mean age of the nurses involved in this study was 34.35 ± 9.37. The nurses’ average age is comparable with other studies in the literature [9, 14]. The results of our study indicated that increasing participant age correlates with greater knowledge, awareness, and application skills about medical artificial intelligence. As participants get older, they tend to have both more work experience and greater variation in scenarios in the health process. Elderly individuals are expected to have greater knowledge and awareness about medical AI tools as they are witnesses to more technological advancements and changes in health care delivery systems for a longer time. In addition, experienced clinician healthcare professionals who have an increased responsibility in the decision-making processes will also feel the need to use the artificial intelligence applications that support diagnosis and treatment processes more actively. Generational differences among nurses can influence their attitudes toward technology, as noted in the literature. In general, younger nurses were found to have more positive attitudes toward technology compared to older nurses [14, 26]. For those who frequently engage with digital tools, the researchers hypothesized that nurses would form a favorable attitude towards the application of artificial intelligence (AI) in nursing. The younger average age of participating nurses supports the assumption that younger nurses exhibit a greater affinity towards technology and are more adaptable than older generations.

The study involved a predominantly female nursing sample size. In our sample, male nurses had higher mean readiness scores than female nurses, whereas no significant gender difference was detected in AI literacy scores. In Turkey and around the world, nursing is a profession mainly done by women, yet previous research indicates that gender-related differences in AI knowledge and readiness are not consistent across settings. For example, a cross-sectional study conducted in Germany identified a significant percentage of male nurses as AI experts and reported gender differences in knowledge levels [15], whereas another Turkish study evaluating nurses’ perceptions and readiness for AI integration in healthcare found female nurses to be more ready [27].

Most of the nurses in our study were single, and it was found that single nurses had higher Artificial Intelligence Literacy and Medical Artificial Intelligence Readiness scores than married nurses. Single nurses have higher artificial intelligence literacy and medical artificial intelligence readiness due to differences in time, work environment, motivation, and access to training. This suggests that family responsibilities may play an indirect role in the process of adapting to professional technology. A study examining the medical artificial intelligence readiness of nurse managers in Turkey reported that single nurses had significantly higher total scores [28]. Similarly, a study conducted by El Gazar et al. (2024) found that single nurses were more likely to accept the use of robots in healthcare than married and divorced nurses [29].

It was determined that the vast majority of nurses participating in our study were bachelor’s degree graduates, and that bachelor’s degree graduate nurses had the highest average scores in artificial intelligence literacy and medical artificial intelligence readiness levels. Graduates may perceive artificial intelligence technologies as a more natural part of their workflow because they have been trained using more computers, simulations and digital clinical applications in their learning processes. Similarly, a study conducted with nursing students reported that participants studying at the bachelor’s degree level had significantly higher artificial intelligence literacy scores than high school students, and that artificial intelligence literacy increased as the class level increased [30].

It was determined that the vast majority of nurses participating in the study had 6 years or more of professional experience and that there was no significant difference in scale score averages based on length of service. Professional experience is not the sole determinant of artificial intelligence literacy and readiness. These levels are shaped more by exposure to technology, opportunities for education, corporate technology infrastructure, and individual desire to learn. Therefore, the fact that no significant difference was observed between experience duration and scale scores is an expected and understandable result. The results of our study are consistent with similar studies in the literature [14, 15, 31].

It is stated that the technological infrastructure status of hospitals and nurses’ exposure to AI applications may vary depending on the type of institution, and that this may affect awareness/attitude [14]. Our research found that less than half of nurses work in private hospitals, and nurses working in private hospitals scored significantly higher on both scales than those working in public institutions. The higher artificial intelligence literacy and medical artificial intelligence readiness scores of nurses working in private hospitals can be attributed to access to technological infrastructure, educational opportunities, the mandatory use of digital systems in workflows, and an innovative organisational culture. This finding demonstrates that the level of digital transformation within organisations is a significant determinant of healthcare workers’ adaptation to technology. This situation stems from the more advanced technological infrastructure in private institutions, greater investment in digital transformation applications, and employees’ increased contact with technology [14, 32].

The literature reports that perioperative nurses are more familiar with AI applications and have higher levels of knowledge due to their increased work with robotic surgery, monitoring systems, and other technological devices [9, 15]. The vast majority of surgical nurses participating in our study work in surgical clinics (inpatient clinics), and nurses working in these clinics were found to have higher AILS and subscale scores. The higher level of artificial intelligence literacy among nurses working in surgical clinics can be attributed to the widespread use of technological equipment, the need for digital support in critical decision-making processes, team-based workflows, and the innovative nature of the surgical field.

Our study found that the vast majority of nurses live in western Turkey, but no significant difference was found in scale scores based on region of residence. However, the average scores of nurses living in cities were found to be significantly higher. No studies in the literature were found that directly examined the effect of place of residence on artificial intelligence literacy and readiness. Based on our findings, it is thought that surgical nurses living in cities have greater access to digital resources and educational opportunities. These findings indicate that geographical and demographic factors must also be considered in the integration of technology into the healthcare system.

In recent years, the rapid proliferation of artificial intelligence (AI) technologies in the healthcare field has significantly impacted nursing practices. Although the literature emphasizes that nurses are among the healthcare professionals who interact most with AI, it is noted that nurses’ active participation in the development, integration, and application processes of these technologies is not yet sufficient [12, 13]. It was determined that the vast majority of nurses participating in our study had heard of the concept of AI before. The fact that most nurses have heard of AI before shows that AI is becoming increasingly prominent in healthcare services and that the nursing profession is actively involved in the digitalization process. Ergin et al. (2022) similarly stated in their research that nurses knew the definition of AI [33]. Furthermore, a study conducted by Özdemir and Biçer (2025) to determine the digital health and AI literacy levels of healthcare workers found that nurses’ AI awareness was above average and that educational level, age, professional experience, the clinic they worked in, and whether they followed technology were among the factors affecting their AI awareness [34]. These findings indicate that the increasing visibility of AI in healthcare services is also being recognized and followed by professionals in the field. AI technologies in the delivery of healthcare services contribute to supporting diagnostic processes, increasing service efficiency, and reducing medical errors [35]. In our study, the vast majority of nurses believe that AI will contribute to the nursing profession and that these technologies will reduce their workload. Furthermore, it was determined that the vast majority of nurses do not think that AI robots will replace nurses in the future and believe that they will benefit the nursing profession. Nurses consider artificial intelligence to be a tool that is supportive, facilitates their functions, and enhances the quality of care; however, due to the human aspect of nursing, they state that AI cannot completely replace nurses. This approach shows that AI with nursing care can develop and progress in a complementary structure and points to a healthy process of adaptation to technology. According to Engin et al. (2023), the implementation of artificial intelligence in robot systems may reduce the workloads of operating room nurses [36]. The findings of our research are in line with the literature, indicating that nurses hold a positive attitude.

The integration of AI technologies in the healthcare area faces a number of challenges and limitations for realization. To successfully embrace technological innovations, it is important to analyze existing barriers and develop novel approaches [7, 37]. Indeed, about one-third of the nurses in this study expressed concern over the future of their profession, which shows reluctance towards AI due to concerns regarding professional identity, ethical responsibility, and human-centric care [7].

Due to the rapid infiltration of AI technologies in the healthcare field, knowledge, skills, and awareness concerning the effective use of these technologies have become an essential issue. In particular, this is important for nurses working in clinical applications [1, 18, 38]. In this study, the average score for surgical nurses’ artificial intelligence literacy (117.19 ± 45.74) was found to be significantly higher than the figures reported in the literature. This may be due to surgical nurses’ interest in digital technologies, their adaptability, and their frequent exposure to the use of such technologies. Furthermore, the fact that the majority of the study group was in the younger age group may also be an explanatory factor in terms of being composed of individuals who are more tech-savvy. Various studies in the literature reveal that nurses’ AI literacy levels are generally moderate. For example, in a study evaluating perioperative nurses, the average AI literacy score of nurses was reported to be moderate (44.35 ± 5.88) [9]. Similarly, a study conducted in China also found that nurses’ artificial intelligence literacy was again at an intermediate level (56.27 ± 8.60) [39].

AI readiness refers to individuals having the necessary knowledge, skills, and attitudes to use AI applications in healthcare effectively [18]. AI technology, which is rapidly spreading and increasing in influence in the healthcare sector, increases the likelihood of nurses encountering such technologies in clinical settings [40]. In our study, surgical nurses’ medical AI readiness levels were found to be above average (70.95 ± 22.22). The fact that surgical nurses’ Medical Artificial Intelligence Readiness Scale scores are above average indicates that these nurses generally have a positive attitude towards adapting to, accepting and using artificial intelligence-based applications. Studies conducted with nurse managers in the literature have reported that nurses’ readiness for AI technologies is at a moderate level [28, 33]. A study conducted with neonatal nurses indicated that nurses’ readiness for AI technologies was at a moderate level.

This study found a significant and strong positive correlation between nurses’ Artificial Intelligence Literacy and their readiness for Medical Artificial Intelligence (rₛ = 0.642, p < 0.001). The presence of moderate to strong correlations between readiness and sub-dimensions such as technical understanding, critical evaluation, and practical application indicates that the level of knowledge and skills related to artificial intelligence is a determining factor in nurses’ adoption of this technology. Nurses must have sufficient functional knowledge (technical knowledge), critical evaluation skills, and practical application skills to adopt and use this technology, among other things. To put it simply, the more knowledgeable and skilled a nurse is, the more prepared he or she is to use the technology. This finding is consistent with other studies that highlight technology literacy as a crucial element of one’s technological adaptation [1, 14, 38, 41]. Further on, the literature details various studies that report cognitive awareness and digital literacy play an essential role in the ability of nurses to use AI and technology [28, 42, 43]. To safely, ethically, and effectively use AI applications, nurses must receive support not just in hardware but also in their cognitive and critical skills. In the modern world of digitization and healthcare services delivery, the introduction of educational content focused on AI in undergraduate and continuing education can aid in better participation of nurses in technology-based decision-making [38, 41].

The correlation between surgical nurse AI literacy levels and readiness for medical AI is high and significant (p-value < 0.01). Most of the nurses who participated said that they know what AI is and that AI can help the nursing profession. In addition, positive significant correlations exist between AI literacy sub-dimensions and levels of readiness, specifically technical understanding, critical evaluation, and practical application.

According to the results, improving AI literacy skills for surgical nurses is crucial for the effective, safe, and ethical use of AI applications. In this sense, content related to artificial intelligence (AI) should be added to the nursing undergraduate and graduate curricula, and nurses’ adaptation to digital health technologies should be supported in the in-service training.

Healthcare organizations can enhance staff adaptation at the institutional level through structured training based on nurses’ technological capacities during the integration of AI-based applications. In such a process, it is necessary to train nurses not just as users but also as health care professionals with an ethical and critical look at AI applications.

It also shows that the artificial intelligence literacy and readiness levels of surgical nurses are influenced by institution type, clinical experience, age, and education. This provides important insights for AI developers. For example, the design of user-friendly interfaces and applications should account for the levels of adaptation to technology of nurses and clinical experiences. In addition, modules and training support for nurses working in various clinical areas (surgery, operating room, wound care, etc.) can be designed and made available. Thus, this paper can enable nurses to use AI systems safely and effectively. Therefore, the findings are not only relevant for nursing education and organizational training programs but also serve as a guide for developers in the process of integrating and developing AI systems into healthcare environments.

Future studies conducted in comparison with nurse groups in different clinical units and focusing in depth on nurses’ experiences using qualitative methods will contribute to a more comprehensive evaluation of perceptions and applications related to artificial intelligence.