Cited by 25Artificial Intelligence in Healthcare and EducationSocial Robot Interaction and HRIAI in Service Interactions

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

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Gumus, Emel, Alan, Handan·2025·International Nursing Review

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.

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Research works printed but not linked: 46

  1. Allen, D. (2014). The invisible work of nurses: Hospitals, organisation and healthcare. London: Routledge. 10.4324/9781315857794 · DOI · Paper page
  2. Başkale, H. (2016) Determining validity, reliability, and sample size in qualitative research. Dokuz Eylül University Faculty of Nursing Electronic Journal (DEUHFED), 9(1), 23– 28
  3. Bates, D.W., Saria, S., Ohno‐Machado, L., Shah, A. & Escobar, G. (2014) Big data in health care: using analytics to identify and manage high‐risk and high‐cost patients. Health Affairs, 33(7), 1123– 1131. 10.1377/hlthaff.2014.0041 25006137 · DOI · Paper page
  4. Bayer, E., Kuyrukçu, A. & Akbas, S. (2019) Evaluation of digital hospital applications from the perspective of hospital employees and managers: a state hospital example. Journal of Academic Research and Studies, 11(21), 22– 36.
  5. Betriana, F., Tanioka, R., Gunawan, J. & Locsin, R.C. (2022) Healthcare robots and human generations: consequences for nursing and healthcare. Collegian. 10.1016/j.colegn.2022.01.008 · DOI
  6. Bini, S.A. (2018) Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care? Journal of Arthroplasty, 33(8), 2358– 2361. 29656964
  7. Broadbent, E., Stafford, R. & MacDonald, B. (2009) Acceptance of healthcare robots for the older population: review and future directions. International Journal of Social Robotics, 1(4), 319– 330. 10.1007/s12369-009-0030-6 · DOI · Paper page
  8. Can, B., Başar, A., Altuntaş, S.B., Özceylan, G. & Kolcu, G. (2021). Artificial intelligence in health education. Medical Journal of Süleyman Demirel University, 28(2), 355– 359. 10.17343/sdutf.876439 · DOI
  9. Çetin, B. & Eroğlu, N. (2020). The value of technology in nursing care and innovation. Kocaeli University Acta Medica Nicomedia, 3(3), 120– 126. https://dergipark.org.tr/tr/pub/actamednicomedia
  10. Colaizzi, P.F. (1978) Psychological research as the phenomenologist views it. In Valle, R.S., & King, M. (Eds.) Existential phenomenological alternatives for psychology. New York: NY Plenum, pp. 48‐ 71.
  11. Creswell, J.W. (2021). Qualitative research methods Bütün, M.D.S.B. (Ed.) (3rd ed.). Political Publishing House.
  12. Dankbaar, M.E. & de Jong, P.G. (2014). Technology for learning: how it has changed education. Perspectives on Medical Education, 3(5), 300– 311. 25163662 PMC4152471
  13. Davenport, T. & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94– 98. PMC6616181 31363513
  14. Duff, J. (2020). Will robots make good perioperative nurses? Journal of Perioperative Nursing, 33(3), e1– e2. 10.26550/2209-1092.1096 · DOI
  15. Fitzpatrick, G. (2004). Integrated care and the working relationships of health care providers: the experience of working together in primary care as a model for telehealth. Behaviour & Information Technology, 23(2), 111– 118.
  16. Geltmeyer, K., et al. (2024) How much do we know about nursing care delivery models in a hospital setting? Nursing Inquiry, 27, e12636. 10.1111/nin.12636 38536152 · DOI
  17. Griffiths, P. & Dall'Ora, C. (2023) Nurse staffing and patient safety in acute hospitals: Cassandra calls again? BMJ Quality & Safety, 32(5), 241– 243. 10.1136/bmjqs-2022-015578 36597996 · DOI
  18. Gümüş, E. & Kasap, E.U. (2022). The level of artificial intelligence anxiety in the health ecosystem. Journal of Artificial Intelligence in Health Sciences, 2(3), 1– 7. 10.52309/jaihs.v2i2.43 · DOI
  19. Gümüş, E.K.E.U. (2021). The future of the nursing profession: robot nurses. Journal of Artificial Intelligence in Health Sciences, 1(2), 20– 25. 10.52309/ja · DOI
  20. Hu, X. & He, X. (2021). Evaluation of the postoperative nursing effect of thoracic surgery assisted by artificial intelligence robot. Contrast Media and Molecular Imaging. 10.1155/2021/3941600 PMC8610671 34867111 · DOI
  21. Korstjens, I. & Moser, A. (2018). Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. European Journal of General Practice, 24(1), 120– 124. 10.1080/13814788.2017.1375092 29202616 PMC8816392 · DOI · Paper page
  22. LeRoy, T.E., Puzzitiello, R., Ho, B., Van Schuyver, P. R. & Kavolus II, J. J. (2023). Orthopaedic trainee views on robotic technologies in orthopaedics: a survey‐based study. The Journal of Knee Surgery, 36(10), 1026– 1033. 10.1055/s-0042-174890 35850132 · DOI
  23. Liang, H.‐F., Wu, K.‐M., Weng, C.‐H. & Hsieh, H.‐W. (2019). Nurses’ views on the potential use of robots in the pediatric unit. Journal of Pediatric Nursing, 47, e58– e64. 10.1016/j.pedn.2019.04.027 31076190 · DOI
  24. Liu, N., Koh, Z. X., Goh, J. & Tan, P.S. (2019). Predicting patients at risk of being admitted to the intensive care unit: a machine learning approach leveraging nationwide electronic health records. Chest, 156(4), 842– 851. 10.1016/j.chest.2019.06.022 · DOI
  25. Locsin, R.C. & Ito, H. (2018). Can humanoid nurse robots replace human nurses? Journal of Nursing Scholarship, 50(6), 634– 641. 10.1111/jnu.12403 30354007 · DOI
  26. McAllister, M., Kellenbourn, K. & Wood, D. (2021). The robots are here, but are nurse educators prepared? Collegian, 28(2), 230– 235. 10.1016/j.colegn.2020.07.005 · DOI
  27. McKinney, S.M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., … & Suleiman, A. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89– 94. 10.1038/s41586-019-1799-6 31894144 · DOI · Paper page
  28. Needleman, J. (2017) Nursing skill mix and patient outcomes. BMJ Quality & Safety, 26, 525– 528. 10.1136/bmjqs-2016-006197 28039393 · DOI
  29. Özdemir, Ş. (2021). New generation threat: Deepfake. Trtakademi, 6(13), 904– 917.
  30. Praveena, K.R. & Sasikumar, S. (2021). Application of Colaizzi's method of data analysis in phenomenological research. Medico‐Legal Update, 21(2), 914– 920.
  31. Pu, L., Moyle, W. & Jones, C. (2020). How people with dementia perceive a therapeutic robot called PARO in relation to their pain and mood: a qualitative study. In Proceedings of the International Conference on Industrial Engineering and Operations Management (March, pp. 1309– 1319). 31738463
  32. Rantanen, T., Lehto, P., Vuorinen, P. & Coco, K. (2018). Attitudes towards care robots among Finnish home care personnel: a comparison of two approaches. Scandinavian Journal of Caring Sciences, 32(2), 772– 782. 10.1111/scs.12508 28833309 · DOI
  33. Renjith, V., Yesodharan, R., Noronha, J.A., Ladd, E. & George, A. (2021) Qualitative methods in health care research. International Journal of Preventive Medicine, 12, 20. 10.4103/ijpvm.IJPVM_321_19 34084317 PMC8106287 · DOI
  34. Robinson, H., MacDonald, B. & Broadbent, E. (2014). The role of healthcare robots for older people at home: a review. International Journal of Social Robotics, 6(4), 575– 591. 10.1007/s12369-014-0242-2 · DOI · Paper page
  35. Saadatzi, M.N., Logsdon, C., Abubakar, M., Das, S., Jankoski, P., Mitchell, H., Chlebowy, D. & Popa, D.O. (2020). Acceptability of using a robotic nursing assistant in health care environments: experimental pilot study. Journal of Medical Internet Research, 22(11), e17509. 10.2196/17509 33180024 PMC7691087 · DOI
  36. Şendir, M., Şimşekoğlu, N., Kaya, A. & Sümer, K. (2019). Nursing in the technology of the future. Health Sciences Journal of Nursing, 1(3), 209– 214.
  37. Sharan, B.M. & Elizabeth, J.T. (2015). Qualitative research: A guide to design and implementation (4th ed.). Jossey‐Bass.
  38. Shorey, S. & Ng, E.D. (2022). Examining characteristics of descriptive phenomenological nursing studies: a scoping review. Journal of Advanced Nursing, 78, 1968– 1979. 10.1111/jan.15244 35405036 PMC9320962 · DOI
  39. Shosha, G. (2012). Employment of Colaizzi's strategy in descriptive phenomenology: a reflection of a researcher. European Scientific Journal, 8(27), 31– 43.
  40. Stalpers, D., Schoonhoven, L., Dall'Ora, C., Ball, J. & Griffiths, P. (2025) ‘Entanglement of nursing care’: a theoretical proposition to understand the complexity of nursing work and division of labour. International Journal of Nursing Studies, 163, 104995. 39818169
  41. Swift, A., Hannes K., Brgles M.M., Dierckx C., Gemignani M., Hendricks L., Huhnen M., Van Goidsenhoven L. & Vrebos H.. (2022). Creative with resources in qualitative research. In Flick U. (Ed.) The SAGE handbook of qualitative research design (Chapter 18, Volume 1), pp. 290– 306. London: Sage.
  42. Syrdal, D.S., Dautenhahn, K., Koay, K.L. & Walters, M.L. (2009). The Negative Attitudes towards Robots Scale and reactions to robot behaviour in a live human–robot interaction study. In Adaptive and Emergent Behaviour and Complex Systems: Proceedings of the 23rd Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB 2009 pp. 109– 115.
  43. Tong, A., Sainsbury, P. & Craig, J. (2007). Consolidated criteria for reporting qualitative research (COREQ): a 32‐item checklist for interviews and focus groups. International Journal for Quality in Health Care, 19(6), 349– 357. 10.1093/intqhc/mzm042 17872937 · DOI · Paper page
  44. Verghese, A., Shah, N.H. & Harrington, R.A. (2018). What this computer needs is a physician: humanism and artificial intelligence. Jama, 319(1), 19– 20. 29261830
  45. Wada, K., Shibata, T., Saito, T., Sakamoto, K. & Tanie, K. (2018). Psychological and social effects of robot therapy in the elderly: comparison of daily and weekly visits in a care house. Nippon Ronen Igakkai Zasshi. Japanese Journal of Geriatrics, 45(2), 365– 374.
  46. Yıldırım, A. & Şimşek, H. (2016). Qualitative research methods in social sciences (10th ed.). Ankara.

Cited by

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Authors
Gumus, E., & Alan, H.
Affiliations
  • Department of Health Management, Faculty of Economics, Administrative and Social Sciences Istinye University Istanbul Turkey
  • Istanbul University-Cerrahpaşa (TR)
Venue
International Nursing Review
Publisher
Wiley
Published
2025-05-27
Volume
72
Issue
2
Pages
e70017-e70017
ISSN
  • 0020-8132 (ISSN-L)
  • 1466-7657
OpenAlex
W4410770993
Cited by
25
References
4
Citations by year
2026: 18 · 2025: 7
FWCI
6.02
Type
journal article
Language
en
Open access
Locations
  • International Nursing Review (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
Keywords
artificial intelligence in healthcare, humanoid robots, attitudes toward robots, physicians, nurses, patients, empathy, individualized care, health policy
MeSH terms
Adult, Aged, Artificial Intelligence, Attitude of Health Personnel, Delivery of Health Care, Female, Humans, Male, Middle Aged, Nursing Staff, Hospital, Physicians, Robotics, Turkey, Qualitative Research

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