Perspectives of physicians, nurses, and patients on the use of artificial intelligence and robotic nurses in healthcare

Abstract

Aim

This study aims to assess the perspectives of physicians, nurses, and patients in Turkey regarding the integration of artificial intelligence (AI) and robotic nurses in healthcare settings while exploring their attitudes toward the use of robots in healthcare delivery.

Background

AI and robotic nurses are increasingly shaping healthcare delivery and influencing clinical decision‐making processes. However, research examining the impact of AI and robotic nurses on nursing practice and patient care remains limited. The attitudes of healthcare professionals and patients are crucial factors for the successful integration and adoption of these technologies in clinical settings.

Method

This qualitative study employed in‐depth individual interviews to explore participants' perspectives. The sample consisted of 13 physicians, 17 nurses, and 15 patients, all recruited from university hospitals, Ministry of Health hospitals, and private healthcare facilities across Turkey. Data were collected using two semistructured interview guides with “Healthcare Workersand Patients”. Ethical approval and informed consent were obtained prior to data collection. The collected data were analyzed with content analysis using MAXQDA Pro 2021 software.

Results

The qualitative findings were organized into four primary themes: “Impact of AI Technologies on Healthcare,” “Use of AI and Robots,” “Receiving Care from Humanoid Robots,” and “Working with Humanoid Robots.” These themes were further explored through 12 subthemes and corresponding codes.

Discussion

Participants who had not yet interacted directly with AI technologies and relied solely on literature or had limited knowledge about the process generally believed that AI and robotic nurses could positively affect healthcare. However, they expressed concerns regarding the inability of these technologies to replicate the human touch. They specifically highlighted the limitations of robots in areas requiring empathy, emotional connection, and personalized care.

Conclusion and implications for nursing and/or health policy

This study explored the perspectives of physicians, nurses, and patients regarding AI and robotic nurses, revealing both anticipated benefits and potential challenges for the healthcare system. The findings indicate the necessity for policies that address the ethical, social, and professional implications of AI and robotics, ensuring that their integration into healthcare practices is aligned with nursing and health policy objectives. It is crucial for international healthcare leaders to collaborate in developing policies that optimize the benefits of these technologies across diverse healthcare settings.

INTRODUCTION

Artificial intelligence (AI), also referred to as machine learning, is defined as systems or machines capable of performing complex tasks by simulating human intelligence and enhancing their own capabilities over time (Özdemir, 2021). AI and robotic technologies have found applications across various sectors, ranging from law and art to tourism and healthcare (Şendir et al., 2019). This technological advancement has had a significant impact on the healthcare system, mirroring changes observed in other professional domains (Şendir et al., 2019). It has become increasingly crucial for healthcare institutions to adopt and integrate technological advancements to maintain competitiveness to enhance the quality of healthcare services, reduce medical error rates, and improve patient satisfaction (Bayer et al., 2019).

In the healthcare sector, AI is used in various applications, including mobile health platforms, scheduling and billing software systems, in‐hospital medication delivery, and, most notably, through the deployment of robotic nurses (Büyükgöze & Dereli, 2020). AI and robotic nurses are increasingly recognized as innovative concepts in healthcare, particularly in nursing practices, ranging from treatment preparation to the implementation of care plans across various domains (Pepito & Locsin, 2019).

Robots have started to support nurses in tasks such as addressing patients' physical needs, assisting with dressing and bathing, providing both physiological and psychological companionship, aiding with daily activities, transferring and repositioning patients, and monitoring their overall health status in settings such as home care, nursing homes, and hospital rooms (Duff, 2020; Hu & He, 2021). Additionally, robots are increasingly employed in interventional processes, including locating veins, drawing blood, inserting intravenous (IV) lines, performing physiological measurements, and recording and interpreting these data. Furthermore, robots facilitate contactless communication between patients and their relatives, enhancing both convenience and safety (Pepito & Locsin, 2019). A study conducted during the COVID‐19 pandemic highlighted that the use of robotic tools in nursing care helped mitigate the risk of virus transmission, as these robots assisted in remotely monitoring and providing care to patients (Taryudi et al., 2022).

Healthcare and nursing services are undergoing a significant transformation with the integration of robotic technologies. As these innovations continue to advance, the roles and responsibilities of nurses are also expected to evolve accordingly (McAllister et al., 2021). The increasing complexity of care and the growing shortage of nursing staff present ongoing challenges in care delivery, necessitating a rethinking and redesign of nursing care provision (Geltmeyer et al., 2024; Griffiths & Dall'Ora, 2023). In this context, the integration of robotic nurses into the workforce represents a transformation process that must not be overlooked in the design of nursing care foundations. Much of the work nurses perform in clinical settings remains invisible (Allen, 2014). For instance, activities categorized as “soft elements,” such as comforting patients, holding their hands, coordinating care, and observing skin color, are often undervalued compared with “formal elements,” such as medication administration or vital sign measurements, which are considered more explicit and structured tasks (Needleman, 2017; Stalpers et al., 2025). Consequently, there is a prevailing perspective that robots can take over simple nursing tasks, allowing nurses to focus on more complex responsibilities. However, this perspective may overlook the intertwined nature of nursing practice. All nursing care activities are, in fact, multilayered, holistic, and inherently complex (Stalpers et al., 2025). In short, nursing tasks cannot be undertaken in isolation, nor can their interconnections be disregarded. For example, while inserting an intravenous line with AI‐assisted applications may appear to be a purely mechanical procedure, nurses consider factors such as the patient's skin color, hydration status, and circulatory system during the process. Similarly, while a robotic nurse may assist in mobilizing a patient, the nurse, before initiating mobilization, takes into account the patient's mobility level, vital signs, fall risk, infection risk, and anxiety levels. In essence, the nursing care process is not limited to direct patient care but encompasses all necessary actions that align with the patient's best interests. Stalpers et al. (2025) argue that workforce allocation based solely on task types is ineffective and that the complexity of nursing work must also be considered (Stalpers et al., 2025). While these discussions highlight the concept of hidden complexity, in the future, nurses may delegate routine “formal elements” to AI‐driven robotic devices. However, they may also need to dedicate more time to these hidden, complex, and critical tasks, particularly those involving direct patient care. Therefore, understanding nurses' perceptions of AI and robotics is crucial in assessing the impact of these technologies on healthcare integration.

The adoption of digital technologies by healthcare institutions is crucial for expanding service delivery, improving patient satisfaction, enhancing employee productivity, refining medical decision‐making processes, reducing human errors, and optimizing the use of resources within healthcare organizations (Can et al., 2021; Gümüş, 2021). Moreover, advancements in healthcare technologies are influencing the behaviors of patients, their families, and the healthcare professionals who interact with these innovations. It is essential to consider the perspectives and existing biases of both the public and healthcare employees for healthcare organizations to effectively implement these technologies and achieve positive outcomes. In addition to ensuring that healthcare professionals are technically prepared to use these applications, it is equally important to address their acceptance and willingness to adopt these technologies (Çetin & Eroğlu, 2020).

In recent years, a substantial volume of literature has emerged globally on AI and robotic technologies. Particularly, a significant portion of these studies employs quantitative research methodologies to examine attitudes, perceptions, and understanding of AI applications, while evaluating the current and anticipated impact of robotic technologies, often employing surveys to obtain empirical data on relevance and implications for the future (Broadbent et al., 2009; LeRoy et al., 2023; Rantanen et al., 2018; Scopelliti et al., 2005). Recent studies by LeRoy et al. (2023) and Rantanen et al. (2018) also employed quantitative methods to investigate the attitudes of healthcare professionals toward robotic technologies. However, these studies often focus on specific professional groups or roles, such as orthopedic interns, pediatric nurses, or home care workers (LeRoy et al., 2023; Rantanen et al., 2018). As a result, these studies fail to offer a comprehensive perspective that includes all key stakeholders in healthcare—namely, physicians, nurses, and patients—thus limiting the scope of their insights into the broader implications of robotic technologies in healthcare settings. Furthermore, research on robotic technologies often highlights diverse intercultural perspectives (Rantanen et al., 2018). It is believed that quantitative studies may be particularly sensitive to cultural differences in this context (Syrdal et al., 2009). Many developing countries, including Turkey, have yet to see widespread implementation of AI and robotic technologies in hospital environments. Individuals in these contexts have primarily encountered AI and robotic technologies through visual media, such as images, brief videos, and films. This disparity poses a potential limitation for quantitative research, as self‐reported data may not fully capture the nuanced attitudes toward these technologies. Consequently, a more effective approach may involve qualitative research that includes the knowledge, perceptions, and attitudes of both the public and healthcare professionals to provide a more comprehensive understanding and to facilitate the successful integration of AI and robotics into healthcare settings in the future years (Liang et al., 2019). In light of the existing literature, this study is one of the first to explore a more comprehensive and integrated understanding of how AI and robotic nurses impact healthcare delivery. This study aims to provide a broader insight into the multifactorial effects of these technologies on clinical practice and patient care by concurrently examining the perspectives of physicians, nurses, and patients.

METHOD

Study design

This study employed the semistructured in‐depth individual interview technique, a qualitative research method that facilitates a deeper understanding of participants' perspectives, emotions, and cognitive processes. Qualitative research is particularly valuable for exploring innovative and emerging topics, as it allows for an examination of participants' perceptions and attitudes toward technologies they have yet to experience. This is especially critical for healthcare professionals and patients who have not yet had direct experience with robotic nurses and AI. While surveys may provide broad, surface‐level data, qualitative methods—such as in‐depth interviews and focus group discussions—facilitate the collection of more detailed, nuanced, and personal insights (Renjith et al., 2021).

As AI and robotic technologies are not yet widely implemented in healthcare across many countries, the existing literature in this field remains limited. Qualitative research is an important tool for revealing participants' perspectives on these emerging technologies, particularly when their views are primarily shaped by literature‐based knowledge rather than direct experience. Understanding these perceptions provides valuable foundational insights that can inform future research and guide the integration of these technologies into healthcare settings (Renjith et al., 2021; Swift et al., 2022)

Qualitative research allows participants to articulate their thoughts in their own words, providing a natural and unrestricted flow of expression. This is particularly valuable when exploring complex and novel technologies, as it enables participants to share their perspectives more freely, even if their views may be influenced by conscious or unconscious biases. Moreover, qualitative research offers flexibility in the data collection process, allowing the study to adapt to the flow of information. This approach enhances the depth of understanding, ensuring a more comprehensive and accurate representation of participants' views (Swift et al., 2022). This study explored the following research questions:

• What are the perspectives of physicians, nurses, and patients regarding the use of AI and robotic technologies in healthcare?

• What are the opinions and attitudes of physicians, nurses, and patients regarding the integration of AI and robotic technologies into healthcare services?

• What are the opinions and attitudes of physicians, nurses, and patients regarding the use of AI and robotic technologies in nursing services?

Setting and participants

The research was conducted between September 2021 and January 2022, involving patients who had received treatment in at least one of the following healthcare settings in Turkey: a university hospital, a Ministry of Health hospital, or a private hospital. Additionally, the study included physicians and nurses who had worked or were currently working in these hospitals. The sample of the study consisted of 45 participants (13 physicians, 17 nurses, and 15 patients) who were either working in or receiving care from the aforementioned types of hospitals in Istanbul and were willing to participate in the research.

The study employed purposive sampling, specifically the maximum variation sampling technique. This method aims to identify common or shared phenomena across diverse contexts and to uncover different dimensions of the issue by exploring the range of perspectives within the sample (Başkale, 2016). To ensure maximum variation, the physician and nurse sample included individuals working across different types of institutions, including university hospitals, Ministry of Health hospitals, and private hospitals. Participants were selected from various positions, departments, and with diverse levels of professional and institutional experience, thereby capturing a broad spectrum of perspectives and insights. The patient sample consisted of individuals from various professions and age groups, all of whom had received healthcare services from at least one department within different types of institutions, including university hospitals, Ministry of Health hospitals, and private hospitals.

While there are varying recommendations regarding the sample size in qualitative research, the emphasis is placed on gaining a detailed understanding through an in‐depth exploration of the phenomenon within a small, focused sample. The goal is not to generalize the findings to a larger population, but rather to provide rich, contextual insights into the experiences and perspectives of the participants. Therefore, “theoretical saturation” serves as the key indicator of sample size adequacy in qualitative research. Data saturation is reached when no new insights or themes emerge from participants' responses, or when the same information is repeated across interviews. At this point, the data collection process is concluded (Sharan & Elizabeth, 2015). In this study, the sample size was determined based on data saturation, which is a fundamental criterion in qualitative research (Creswell, 2021). The study was concluded after conducting 45 in‐depth individual interviews (13 physicians, 17 nurses, and 15 patients) when data saturation was reached.

Data collection

The data collection tools employed in this study included the “Semi‐Structured In‐Depth Individual Interview guide for Physicians and Nurses” and the “Semi‐Structured In‐Depth Individual Interview guide for Patients.” Both interview guides consisted of open‐ended questions designed to gather participants' personal and professional information, explore their experiences with technological advancements in healthcare, and assess their opinions on AI and robotic nurses.

Prior to applying the interview questions, expert feedback was received from two faculty members with expertise in qualitative research. Based on their input, the questions were refined and revised to ensure clarity, relevance, and alignment with the study's objectives. To assess the appropriateness and clarity of the interview questions, the first author (R1), a doctoral student in nursing management with 10 years of clinical experience and 5 years in academia, and the second author (R2), a faculty member in nursing management with 18 years of clinical experience and 6 years of academic experience, both trained in qualitative data collection and analysis, conducted three pilot interviews. These interviews involved a physician, a nurse, and a patient. The feedback from these pilot interviews was used to refine and adjust the questions for clarity and relevance (Korstjens & Moser, 2018). The data obtained from the pilot interviews were not included in the final analysis of the study. Subsequent interviews were conducted by the first author.

Interviews were conducted face‐to‐face with participants who were available for in‐person interviews, while those who were unable to participate in person were interviewed online at a time and location that suited the participant. The data were recorded using a digital voice recorder and transcribed verbatim. To ensure participant confidentiality, the transcripts were anonymized, and codes were assigned to each participant (e.g., Doctor = D1, D2; Nurse = N1, N2; Patient = P1, P2…). The interviews lasted an average of 45.2 minutes (min = 34, max = 67). The audio recordings of the qualitative data were transcribed immediately after each interview, before proceeding to the next one, ensuring that the data were accurately captured and ready for analysis (StreubertSp& Carpenter, 2019).

Rigor and trustworthiness

The credibility of research results is considered one of the most crucial criteria in scientific inquiry. In this study, the criteria suggested by Lincoln and Guba (1989) were applied to ensure rigor. They argued that, in qualitative research, it is more appropriate to use the concepts of credibility and transferability instead of validity, and consistency and confirmability instead of reliability (Yıldırım & Şimşek, 2016). Each participant voluntarily participated in the study, and the researcher had no personal or managerial relationship with any of the participants. To foster an open and honest dialogue, participants were encouraged to share their thoughts and experiences freely, with the understanding that there were no “right” or “wrong” answers to the interview questions. The researchers also took detailed notes on participants' gestures, behaviors, reactions, and the interview environment, as well as any pauses or breaks that occurred during the interviews. To ensure consistency, all interviews were conducted by R1. Each researcher actively and independently contributed to the data analysis process. A semistructured interview guide was employed to maintain consistency across interviews. Additionally, the interview transcripts were sent to the participants for statement approval, allowing them to verify the accuracy of their responses and ensuring the credibility of the findings. Participants' statements were quoted verbatim to allow readers to assess the transferability of the study's findings to their own contexts.

Data analysis

The research data were analyzed using the content analysis method. The steps of descriptive phenomenological analysis, as outlined by Colaizzi (1978), were followed to systematically interpret the data. MAXQDA 2020 software was employed to assist in the data analysis process (Shosha, 2012).

The method proposed by Colaizzi (1978) consists of seven key steps: (Allen, 2014) becoming familiar with the data, (Başkale, 2016) identifying significant statements, (Bates et al., 2014) formulating meanings, (Bayer et al., 2019) clustering themes, (Betriana et al., 2022) developing a comprehensive description, (Bini, 2018) establishing the fundamental structure, and (Broadbent et al., 2009) verifying the accuracy of the findings (Praveena & Sasikumar, 2021; Shorey & Ng, 2022). At the conclusion of the Colaizzi process, both researchers produced detailed descriptions and definitions of the identified themes, which were further refined to generate subthemes. The transcribed texts were systematically coded, and commonalities were grouped to form overarching themes (categories) that would outline the research findings.

Ethical considerations

Data collection for the research was initiated after obtaining ethical approval from the university's ethics committee (Approval Date: April 5, 2021; Decision No: 2021/123). Prior to the interviews, participants were provided with detailed information about the study, and informed consent, including permission for audio recording, was obtained from all participants. The study was reported in accordance with the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines (Tong et al., 2007).

RESULTS

A total of 45 participants, comprising physicians, nurses, and patients, participated in the study. The participating physicians were coded from D1 to D13, nurses from N1 to N17, and patients from P1 to P15. The descriptive characteristics of the participants are presented in Table 1.

TABLE 1.

Descriptive characteristics of participants.

Code	Gender	Age (year)	PE (year)	Unit	Code	Gender	Age (year)	PE (year)	Position	Code	Gender	Age (year)	PE (year)	Profession
D1	M	68	48	Pediatrics	N1	F	28	10	CN	P1	F	41	20	Education coordinator
D2	M	39	15	Genetics	N2	F	40	21	MN	P2	F	42	20	Human resources
D3	M	56	32	Manager	N3	F	41	21	CN	P3	F	34	12	Health tourism
D4	M	36	14	Urology	N4	F	29	8	MN	P4	F	28	6	Dietitian
D5	M	54	30	Anatomy	N5	F	36	12	CN	P5	F	38	18	Communication coordinator
D6	M	38	14	Family physician	N6	F	28	7	CN	P6	F	25	1	Student
D7	M	51	27	ENT	N7	F	26	6	CN	P7	F	24	1	Student
D8	M	53	29	Family physician	N8	F	41	21	MN	P8	F	27	5	Academic
D9	M	36	14	Family physician	N9	F	41	21	CN	P9	M	42	20	Manager
D10	M	61	42	Urology	N10	F	47	25	CN	P10	F	28	8	Teacher
D11	M	50	25	Chief physician	N11	F	40	21	CN	P11	F	35	12	Lawyer
D12	F	48	23	Family physician	N12	F	41	20	CN	P12	M	35	10	Trainer
D13	M	43	21	Obstetrics	N13	M	26	5	CN	P13	M	57	38	Businessman
					N14	F	36	15	CN	P14	F	30	10	Sales consultant
					N15	F	27	6	CN	P15	M	42	25	Sound technician
					N16	M	27	6	CN
					N17	F	42	21	CN

D = doctor, N = nurse, P = patient, professional experience = PE, M = male, F = female, MN = manager nurse, CN = clinical nurse.

According to the data presented in Table 1, the average age of the physicians participating in the study was 48.69 years (min–max: 36–68 years), and their average duration of professional experience was 25.69 years (min–max: 14–49 years). Among the participating physicians, 92.31% were male, while 7.69% were female. The average age of the participating nurses was 35.05 years (min–max: 28–47), and their average duration of clinical experience was 14.47 years (min–max: 5–25 years). Among the participating nurses, 88.24% were female, and 11.76% were male. The average age of the participating patients was 35.2 years (min–max: 24–57), with 73% being female.

As a result of the content analysis of the data obtained from the individual in‐depth interviews, 4 main themes and 12 subthemes were identified (Table 2).

TABLE 2.

Themes, subthemes, and sample statements of participants.

Theme	Subtheme	Sample statements of participants
1. Impact of Artificial Intelligence Technologies on Healthcare Services	1.1 Reduction of Workload	“In general, since I will be using the robot, it will relieve me in many tasks, reducing my workload. It will also allow me to rest more.” (N9) “For example, if you have 10 people working in an intensive care unit and you have 1 robot, this could reduce the number to 7 or 8.” (D2)
	1.2 Support for Healthcare Practices	“Electronic beds, air mattresses, and all auxiliary equipment based on the quality and sustainability of care are very important for us. The development of biomedical engineering in this area has also made our work easier and has been beneficial, of course.” (N13) “Some studies are underway that will, even in our daily lives, have all of our health data like height, weight, heart disease risk, etc., in e‐health records. These are infrastructures that may provide us with very different health services in the future, and I see that there are studies being conducted in these areas.” (P1)
	1.3 Adaptation to Rapid Developments and Changes	“It's very fast. If one is not continuously involved, everything is progressing at a speed that can be described as very difficult to keep up with. There are times when I hear things and feel like I don't know anything at all when they mention something that has been done.” (N4) “Actually, I think it's going in a very positive direction. Especially since we've transitioned to the software aspect… I can actually feel that there are significant changes in nursing with the entry of artificial intelligence into our lives.” (P4)
	1.4 Time Savings	“So, imagine that during mobilization, a nurse spends half an hour with a patient; if a robot is doing that in the meantime, the nurse could focus more on the issues of another patient in the next room. I think this would definitely be something that relaxes the nurse.” (N10) “I can't say for sure since I haven't experienced it before, but especially in the future, with the high number of patients and the inability of state hospitals to keep up, the presence of robots could speed up many processes. Additionally, since they will be stronger—perhaps in terms of moving them—robots could help facilitate faster control of processes.” (P2)
	1.5 Support for Education	"… I believe that robots, as a result of good training and coding, can also be considered good nurses in terms of education. I think robots could be very effective as educational nurses.” (P15)
2. Use of Artificial Intelligence and Robots	2.1 Diagnostic and Treatment Services	“We took various measurements of the patient: blood pressure, temperature, pulse, and risk assessment scales for fall risk, pressure ulcer risk, etc. We collected their data. Normally, we think and make decisions based on this data. We develop a care plan accordingly. Nursing definitions emerge, and in the background, software could help interpret the data obtained from these patients.” (N3) “If we regularly donate blood, it could track changes in the blood test results and provide guidance for the treatment to be applied based on previous patient data.” (P2)
	2.2 Nursing Services	“In the past, for example, we used manual blood pressure monitors more often, but now we have portable monitors. We have devices that give alarms by themselves. For instance, we frequently use this in intensive care units. I can say that artificial intelligence is one of the areas where we use it a lot.” (N11) “However, when commanded, robots can perform part of nursing care. For example, they can wipe the patient's hand, clean their face, do those tasks, and administer medications. … They must work in conjunction with nurses; the robot cannot do it all by itself.” (P12)
	2.3 Use in Other Services	“At the very least, for example, I would like my blood test results to come out earlier. This is very much related to the quality of technology.” (P4) “Artificial intelligence doesn't understand patients and diseases, but it can be useful for data collection, analyzing them, or, I don't know, tasks like robots bringing and taking things from one place to another.” (D11)
3. Receiving Care from Humanoid Robots	3.1 Psychosocial Support	“First of all, it cannot understand the patient's emotions. Even when simply removing a dressing, while we understand the pain sensation, how can a robot grasp that feeling…” (N6) “The healthcare sector is a service sector; in Turkey, patients will want to see a physical person in front of them, and the first person they encounter is the nurse.” (D4)
	3.2 Patient Safety	“… perhaps patient safety could be better ensured. Because nurses still have very demanding working hours, which haven't changed much. They work under very poor conditions… There are many things that threaten patient safety that might go unnoticed by them; perhaps because robots lack this human factor, I think they would develop much better.” (N16) “Since everything is recorded, it will prevent anything related to the patient from being overlooked. Therefore, of course, the advancement of technology will assist in nursing and patient care services. It will benefit physicians in the same way. It will eliminate abstract data and ensure that everything is recorded. It will provide significant advantages to physicians, patients, and their families.” (P6)
4. Working with Humanoid Robots	4.1 Willingness and Form of Work	“… Honestly, I'm not ready for that. I've always viewed robots as products of artificial intelligence, as human creations. I don't find it logical for something that is a product of humans to manage humans. I wouldn't want that.” (N5) “Of course, I don't want it to take my place or be my manager, but if the robot is going to be my assistant, I will manage it.” (N7) “Yes, I would like to work with it; there's no problem with that. It would make my job easier, I could do things faster, and I would learn something.” (D2) “Let it be a coworker, but not a manager… Because management is a very different matter.” (N1) “No, I'm afraid. I wouldn't want to work together. Because of artificial intelligence. What will that intelligence become afterward? What will it do, and could it turn into a threat for me? Artificial intelligence, I'm afraid of it. It's something that is superior to me.” (N17)
	4.2 Impact on Work Life	“The concepts of communication and teamwork in the future need to be well structured. However, I think it's important to design the teamwork concept properly. When such a restructuring is implemented in the work environment, I believe job descriptions must be well defined, and the team should be properly assembled.” (P12) “Of course, they will reduce the number of nurses… Robot nurses will take over some of the nurse's tasks, and because certain tasks are being handled, some nurses will be dismissed.” (N7) “… It will definitely affect salaries. It will affect them negatively… They'll say, ‘We no longer need 5 nurses; 3 nurses will suffice, and let's lower their salaries from point A to point B,’ in my opinion!” (D4) “…I think robots can act as assistants to nurses. They won't touch the patient directly or take over my core nursing roles, but they can assist with tasks like arranging the patient's room or making the bed, like a nursing assistant. However, I wouldn't want the robot to administer medication to the patient.” (N7) “From an ethical perspective, while it may seem like a convenience for human life… it doesn't sit well with me ethically, and it could pose problems.” (N8) “From an ethical perspective, I don't think robots will pose any problems… I think it's very logical. But we should always work together under the supervision and oversight of a nurse. The ethical approaches, emotional sensitivity, and the way a nurse interacts with a patient—these are things that no robot or artificial intelligence can replace or fulfill.” (N5)

Theme 1: The impact of AI technologies on health services

This main theme was developed to capture the perspectives of physicians, nurses, and patients regarding the impact of recent technological advancements, particularly AI, on healthcare services. Participants indicated that one of the effects of AI technologies on healthcare services is the reduction in workload (Table 2, Subtheme 1.1). Participants stated that the reduction in workload would particularly have a positive impact on nurses, allowing them to allocate more time to direct patient care and other critical tasks.

“First of all, it reduces the workload for nurses and makes work more comfortable for us—that's how I see it… It not only helps with the workload but also, I believe, improves patient comfort too.” (N2)

Participants mentioned that AI technologies support healthcare applications (Table 2, Subtheme 1.2). The reported views suggested that AI technologies can make positive contributions to healthcare applications and enhance the role of nurses within the healthcare sector.

“As long as nurses know how to make the most of these technologies, meaning if they use them effectively… I believe the role of nurses in healthcare will become even stronger.” (D1)

Participants highlighted both the challenges associated with adapting to the rapid development of AI technologies and their advantages (Table 2, Subtheme 1.3).

“Digital transformation is happening in healthcare. For example, in our hospital, we have different databases, like the clinical portal. Whether it's the system we use for document management or the one for entering patient information… We're really going through a technological change.” (N5)

Participants indicated that AI applications facilitate time savings in healthcare services and accelerate various processes (Table 2, Subtheme 1.4).

“Technological advancement has made a big difference in how we use our time. The difference between manually measuring blood pressure and doing it automatically is huge. It helps me do my job more efficiently and use my time better.” (N9)

It was reported that the use of AI technologies in nursing education enhances the effectiveness of training processes and supports learning outcomes (Table 2, Subtheme 1.5).

“When used in education, like with simulations, artificial intelligence makes learning easier for nurses. During the pandemic, when in‐person training was stopped, we really saw the need for AI tools. In education and training student nurses, we actually turned to technology.” (N5)

Theme 2: Use of AI and robots

This theme examined the perspectives of physicians, nurses, and patients participating in the study regarding the use of AI and robots in healthcare. Participants indicated that the use of AI and robots in healthcare is primarily focused on “Diagnosis and Treatment Services” (Table 2, Subtheme 2.1). They expressed that AI, and robots could particularly assist in nursing diagnosis processes by providing support in information recording and could contribute to error‐free application during treatment preparation stages.

“…Of course, if it's a robot for preparing medications, it would make the job much easier… especially when it comes to calculating the dosages for chemotherapy in cancer patients…” (D2)

Participants indicated that AI and robots could also be utilized in “Nursing Services,” particularly in intensive care units, operating rooms, and inpatient clinics, for tasks such as measuring blood pressure, inserting IV lines, and transporting patients (Table 2, Subtheme 2.2).

“There's a simple device that helps find the right vein— you just hold it against the patient's arm, and it accurately shows the anatomical structure and veins, making it impossible to miss the right one. It makes inserting an IV line much easier, which is really important for nurses working in the wards.” (D6)

Additionally, participants stated that AI and robots are also employed in other healthcare services, including pharmacy services, radiological procedures, laboratory processes, and hospitality services (Table 2, Subtheme 2.3).

“A robot that prepares chemotherapy medications reduces the nurses' exposure to radiation.” (N1)

Theme 3: Receiving care from humanoid robots

Participants expressed both positive and negative views regarding receiving care from humanoid robots during the individual in‐depth interviews. One key aspect of these views concerned whether humanoid robots could provide psychosocial support (Table 2, Subtheme 3.1).

“……… I mean, I would want the nurse to care for me with compassion and to have conversations with me… There is such a thing as nurse compassion.” (D1)

Participants agreed that humanoid robots play a crucial role in enhancing patient safety and reducing risks during patient care processes (Table 2, Subtheme 3.2).

“…a living human has hormones, a voice, happiness, and other emotions. That day, the nurse might come into the medication preparation unit feeling unhappy, maybe due to something on their mind, like their child or spouse. In that case, they might make a mistake or overlook something. If robots were preparing the medications, such issues wouldn't occur…” (D2)

Theme 4: Working with humanoid robots

The final theme explored in this study concerned working with humanoid robots. Participants indicated a potential willingness to work with humanoid robots, while emphasizing the importance of its structure (Table 2, Subtheme 4.1). The majority of participants expressed reluctance to be managed by robots; however, they also acknowledged that managing robots and establishing collaboration may provide various benefits.

“… Therefore, I think robots are just. A robot is devoid of the positive and negative emotions inherent in human feelings; it is programmed to be correct and singular. Therefore, yes, I believe a robot can be a manager.” (N13)

Participants stated that humanoid robots could have various impacts on the work environment (Table 2, Subtheme 4.2.). Among the most emphasized points were the provision of support in care, the creation of new forms of employment, the impact on teamwork dynamics and wages, changes in job descriptions, and the need for ethical and legal regulations. Participants from the physician and patient groups shared fewer opinions on these topics.

“Let the robot take over my heavy workload. Like I said, let it turn the patient, position them, prepare chemotherapy drugs, and transport the medication to where it needs to go. Let it handle those tasks, but don't interfere too much in my personal spaces.” (N1)

DISCUSSION

The participants in this study highlighted several key benefits of AI technologies, including a reduction in nurses' workload, an acceleration of work processes, enhanced support for healthcare practices, and time savings. These findings align with previous research that emphasizes the potential of AI to streamline healthcare delivery, improve efficiency, and alleviate certain burdens faced by healthcare professionals, particularly nurses. Similarly, the literature indicates that AI technologies, particularly in managing routine and repetitive tasks, enhance efficiency by alleviating the workload of healthcare professionals (Davenport & Kalakota, 2019; Gümüş & Kasap, 2022). For instance, the integration of electronic health records (EHRs) and automated data entry systems has significantly reduced the administrative burden on nurses, facilitating more efficient data management (Topol, 2019). Additionally, AI has proven to offer substantial benefits in patient care, particularly through clinical decision support systems (CDSS) and telemedicine applications (McKinney et al., 2020). Many AI‐based systems, such as triage systems, facilitate the rapid assessment of patients and enable timely interventions (Liu et al., 2019). One study suggests that delegating routine tasks in clinical operations, which can be clearly defined and programmed, to robots will enable nurses to dedicate more time to direct patient care, thus enhancing their interaction with patients (Betriana et al., 2022).

Despite the benefits, the rapid development and integration of AI technologies in healthcare present significant challenges in terms of adaptation, often leading to stress and anxiety among healthcare employees (Verghese et al., 2018). This situation highlights the growing need for continuous education and professional development among healthcare professionals (Bates et al., 2014). The literature emphasizes the significant role of AI in health education, particularly through simulation‐based learning and virtual reality applications (Dankbaar & de Jong, 2014). Especially following the COVID‐19 pandemic, the rise of online education, along with the integration of AI and robotic technologies, as well as the use of computers and mobile applications, has positively impacted the learning and teaching process, while also expanding access to education (Ronquillo et al., 2021).

Participants noted that AI and robots can be effectively utilized in various aspects of healthcare, including diagnosis, treatment, nursing, and other healthcare services. The finding that AI and robots support nurses and physicians in diagnostic and treatment processes is consistent with existing literature. AI‐based imaging analysis systems and robotic surgery applications have been shown to enhance the accuracy and efficiency of diagnostic and treatment processes (Hashimoto et al., 2018). The use of AI and robots in nursing services, particularly in intensive care units, operating rooms, and inpatient clinics, is also emphasized. The use of robotic technologies can reduce physically demanding tasks for nurses, thereby helping to prevent physical injuries (Saadatzi et al., 2020).

AI and robotic technologies are also considered as tools that can enhance the organization of routine clinical practices and treatment processes, providing nurses with the necessary information to make accurate decisions (Bini, 2018). The literature similarly highlights that these technologies improve the quality of nursing care and enhance patient safety (Locsin & Ito, 2018). Furthermore, AI applications in logistics and administrative tasks have been shown to increase the overall efficiency of healthcare services (Wang et al., 2019). In laboratory settings, robots can be assigned repetitive and routine tasks, which further improves operational efficiency (Miller, 2020).

Participants in the study emphasized that humanoid robots could provide psychosocial support and contribute to improving patient safety. The finding that humanoid robots provide psychosocial support and enhance patient morale is consistent with existing literature. In the context of elderly care, research has shown that humanoid robots play a significant role in mitigating issues such as loneliness and depression (Wada et al., 2018). In addition, it has been suggested that robots can improve patients' emotional well‐being, reduce pain, and thereby support nurses in delivering more effective care (Pu et al., 2020). Robots have also been identified as valuable tools for enhancing patient safety, particularly in preventing falls and reducing medication errors (Broadbent et al., 2009).

Participants indicated a willingness to collaborate with humanoid robots; however, they also recognized the potential challenges associated with their integration into healthcare settings. Similarly, a study has demonstrated that robot‐assisted work environments can enhance job satisfaction among healthcare professionals and help reduce stress levels (Robinson et al., 2014). However, it also discusses the challenges that workers encounter in adapting to new technologies, as well as the difficulties experienced during the adjustment period (Fitzpatrick, 2004).

Limitations

The generalizability of the study's findings may be limited by several factors. First, the data collected are based on participants' personal opinions and experiences, which could introduce subjective bias. Second, AI and robotic technologies are becoming more integrated into the healthcare environments of developed countries, but their adoption remains limited in developing countries such as Turkey. Consequently, the participants' relatively restricted exposure to these technologies may have narrowed the scope of the findings. Lastly, the study's focus on participants residing in a single city further limits the ability to generalize the results to broader populations or diverse healthcare settings.

CONCLUSION

This study was conducted to assess the perspectives of physicians, nurses, and patients regarding the use of AI and robot nurses in healthcare services. While research focusing on specific groups has been carried out, this study provides valuable insights by presenting an integrated perspective that includes the views of all three stakeholders—physicians, nurses, and patients. Furthermore, given that the participants had no prior exposure to AI applications or robotic technologies, understanding their individual attitudes is a critical factor for the successful integration of these technologies into healthcare settings in the future. The research findings highlight the impacts of AI and robotic technologies on healthcare from various perspectives. The findings suggest that these technologies provide substantial benefits, such as reducing workload, supporting healthcare practices, facilitating adaptation to rapid advancements and changes, saving time, and enhancing educational opportunities.

AI and robotic technologies hold the potential to drive transformative changes in healthcare services. However, it is crucial to acknowledge the challenges encountered during the technological adaptation process and to address the needs of healthcare professionals throughout this transition. Future research should aim to further validate these findings through the use of larger, more diverse samples and a variety of data collection methods, thereby providing more comprehensive results and valuable insights for the effective integration of AI and robotic technologies in healthcare settings. The integration of AI and robotic nurses in healthcare is of significant importance for the future development of the nursing profession.

Implications for nursing and health policy

The integration of AI and robotic technologies into healthcare carries transformative implications for both nursing practice and health policy. Research suggests that these innovations are expected to reduce the workload of nurses, enabling them to focus on more complex and critical care tasks. This shift not only improves the quality of patient care but also enhances workforce planning by facilitating the more strategic allocation of human resources. Furthermore, AI and robotic systems offer crucial support in patient monitoring, diagnosis, and treatment, which can significantly reduce error rates and enhance overall care delivery. The increased efficiency afforded by these technologies translates into considerable time savings for healthcare professionals, contributing to a more productive and responsive healthcare environment. Additionally, the application of AI in nursing education—through AI‐assisted simulations and virtual reality technologies—enhances the learning experience, equipping nurses with the advanced skills required to navigate the evolving healthcare landscape. Therefore, the integration of AI and robotics is essential for advancing the nursing profession and shaping effective health policies, highlighting the importance of ongoing exploration and integration of these technologies within healthcare systems.

It is essential to adopt several key strategies to effectively capitalize on the potential of AI and robotic technologies in healthcare. First, it is essential to prioritize the awareness and training of nurses regarding these technologies. Educational programs should be designed to instruct nurses on the effective and safe implementation of AI and robotic systems in clinical environments, ensuring they are adequately prepared to utilize these advanced tools. Second, healthcare institutions must prioritize the integration of these technologies into nursing workflows, which will help reduce nurses' workloads while simultaneously improving the quality of patient care. Additionally, ethical considerations and safety protocols should be emphasized to safeguard patient data privacy and security, while also establishing clear guidelines for the responsible and ethical use of these technologies. Finally, a multidisciplinary approach should be adopted to ensure the effective implementation of AI and robotic technologies in healthcare, fostering collaboration among physicians, nurses, engineers, and information technology specialists. The nursing profession can optimize the benefits of emerging technologies, leading to improved patient care and contributing to the advancement of health policy by embracing these recommendations.

AUTHOR CONTRIBUTIONS

Study design: Emel Gumus and Handan Alan. Data collection: Emel Gumus. Data analysis: Emel Gumus and Handan Alan. Study supervision: Handan Alan. Manuscript writing: Emel Gumus and Handan Alan. Critical revisions for important intellectual content: Emel Gumus and Handan Alan.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

FUNDING STATEMENT

The authors did not accept any funding from public or private facilities for performing this study.

PERMISSIONS

Informed consent was obtained from all participants who took part in the study.

SUBMISSION DECLARATION

This study was performed within the scope of the thesis in Istanbul University–Cerrahpaşa Institute of Doctorate Program, Department of Nursing Management in 2023 in Turkey.

Gumus, E. & Alan, H. (2025) Perspectives of physicians, nurses, and patients on the use of artificial intelligence and robotic nurses in healthcare. International Nursing Review, 72, e70017. 10.1111/inr.70017