Perceptions,Attitudes,and Expectations of Surgical Patients Toward Artificial Intelligence–Supported Nursing Care: A Mixed Methods Study

Abstract

Introduction:

Artificial intelligence (AI) is increasingly influencing nursing and surgical care. Although AI-supported systems may improve efficiency, they also raise ethical and humanistic concerns, highlighting the importance of patient perspectives.

Methods:

An explanatory sequential mixed methods design was used. Quantitative data were collected from 312 postoperative surgical patients to assess knowledge and attitudes toward AI-supported care. Based on these results, 15 participants were selected for in-depth qualitative interviews using purposive sampling. Descriptive qualitative content analysis was applied, and the findings were integrated.

Results:

Patients’ attitudes toward AI were generally moderate and cautious. Most participants reported limited knowledge and perceived AI as a media-driven and abstract concept. Major concerns included loss of human touch, system errors, data security, and trust. AI was viewed as a supportive tool rather than a replacement for nursing care.

Discussion:

Acceptance of AI-supported nursing care is shaped by cultural values, trust, human-centered expectations, requiring empathy and professional oversight.

Keywords

artificial intelligence nursing care surgical patients patient perception patient attitudes mixed methods

Get full access to this article

View all access options for this article.

References

Ahadian

Liu

Guan

(2025). Ethics of trustworthy AI in healthcare: Challenges, principles, and practical pathways. Neurocomputing, 661, 131942. https://doi.org/10.1016/j.neucom.2025.131942

Antes

A. L.

Burrous

Sisk

B. A.

Schuelke

M. J.

Keune

J. D.

DuBois

J. M.

(2021). Exploring perceptions of healthcare technologies enabled by artificial intelligence: An online, scenario-based survey. BMC Medical Informatics and Decision Making, 21, 221. https://doi.org/10.1186/s12911-021-01586-8

Bajwa

Munir

Nori

Williams

(2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), Article e188–e194. https://doi.org/10.7861/fhj.2021-0095

Braun

Clarke

(2013). Successful qualitative research: A practical guide for beginners. Sage.

Cohen

(2013). Statistical power analysis for the behavioral sciences. Routledge. https://doi.org/10.4324/9780203771587

Dailah

H. G.

Koriri

Sabei

Kriry

Zakri

(2024). Artificial intelligence in nursing: Technological benefits to nurses’ mental health and patient care quality. Healthcare, 12(24), 2555. https://doi.org/10.3390/healthcare12242555

Faiyazuddin

Rahman

S. J. Q.

Anand

Siddiqui

R. K.

Mehta

Khatib

M. N.

Gaidhane

Zahiruddin

Q. S.

Hussain

Sah

. (2025). The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency. Health science reports, 8(1), e70312. https://doi.org/10.1002/hsr2.70312

Ferreira

J. C.

Elvas

L. B.

Correia

Mascarenhas

(2025). Empowering health professionals with digital skills to improve patient care and daily workflows. Healthcare, 13, 329. https://doi.org/10.3390/healthcare13030329

Fritsch

S. J.

Blankenheim

Wahl

Hetfeld

Maassen

Deffge

Kunze

Rossaint

Riedel

Marx

Bickenbach

(2022). Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients. Digital Health, 8, 20552076221116772. https://doi.org/10.1177/20552076221116772

10.

Gundlack

Thiel

Negash

Buch

Apfelbacher

Denny

Christoph

Mikolajczyk

Unverzagt

Frese

(2025). Patients’ perceptions of artificial intelligence acceptance, challenges, and use in medical care: Qualitative study. Journal of Medical Internet Research, 27, Article e70487. https://doi.org/10.2196/70487

11.

Guo

Zuo

Sun

Qian

(2025). Elevating surgical outcomes: A comprehensive analysis of innovative nursing practices in intraoperative care across diverse specialties. Frontiers in Medicine, 12, Article 1665160. https://doi.org/10.3389/fmed.2025.1665160

12.

Hajiheydari

Delgosha

M. S.

Saheb

(2025). AI in medical diagnosis: A contextualised study of patient motivations and concerns. Social Science & Medicine, 371, 117850. https://doi.org/10.1016/j.socscimed.2025.117850

13.

Huter

Krick

Domhoff

Seibert

Wolf-Ostermann

Rothgang

(2020). Effectiveness of digital technologies to support nursing care: Results of a scoping review. Journal of Multidisciplinary Healthcare, 13, 1905–1926. https://doi.org/10.2147/JMDH.S286193

14.

Kaya

İşçi

(2025). Validity and reliability study of general attitude towards artificial intelligence scale in healthcare. Fenerbahçe University Journal of Health Sciences, 5(1), 52–63. https://doi.org/10.56061/fbujohs.1531710

15.

Lee

C. M. Y.

Thomas

Norman

Wells

Shaw

Nesbitt

Robinson

(2022). Educational attainment and willingness to use technology for health and to share health information: The reimagining healthcare survey. International Journal of Medical Informatics, 164, 104803. https://doi.org/10.1016/j.ijmedinf.2022.104803

16.

Longoni

Bonezzi

Morewedge

C. K.

(2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013

17.

Mayoh

Onwuegbuzie

A. J.

(2015). Toward a conceptualization of mixed methods phenomenological research. Journal of Mixed Methods Research, 9(1), 91–107. https://doi.org/0.1177/1558689813505358

18.

Molina-Mula

Gallo-Estrada

(2020). Impact of nurse–patient relationship on quality of care and patient autonomy in decision-making. International Journal of Environmental Research and Public Health, 17(3), 835. https://doi.org/10.3390/ijerph17030835

19.

Osnat

(2025). Patient perspectives on artificial intelligence in healthcare: A global scoping review of benefits, ethical concerns, and implementation strategies. International Journal of Medical Informatics, 203, 106007. https://doi.org/10.1016/j.ijmedinf.2025.106007

20.

Schepman

Rodway

(2020). Initial validation of the general attitudes towards artificial intelligence scale. Computers in Human Behavior Reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014

21.

Topol

E. J.

(2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25, 44–56. https://doi.org/10.1038/s41591-018-0300-7

22.

Wang

Asan

Mansouri

(2023). Perspectives of patients with chronic diseases on future acceptance of AI-based home care systems: Cross-sectional web-based survey study. JMIR Human Factors, 10(1), Article e49788. https://doi.org/10.2196/49788

23.

Young

A. T.

Amara

Bhattacharya

Wei

M. L.

(2021). Patient and general public attitudes towards clinical artificial intelligence: A mixed methods systematic review. The Lancet Digital Health, 3(9), Article e599–e611. https://doi.org/10.1016/S2589-7500(21)00132-1