From data analysis to robotic surgery: nurses’ ranking of autonomous AI in clinical care
Abstract
Background: Artificial intelligence (AI) is entering clinical pathways, yet acceptance varies by clinical task. From a public health perspective, understanding acceptable AI applications is important for safe implementation. This study compared and ranked nurses’ acceptance of different AI applications in clinical practice.
Methods: A cross-sectional online survey was conducted among nurses in Poland (N = 800). Quota sampling obtained a large, demographically structured sample. Respondents assessed eight AI applications in diagnostics and treatment on a 1-10 scale, assuming no or minimal involvement of medical staff. Ordinal items were analyzed using the Friedman test, followed by Bonferroni-corrected pairwise comparisons. The study was funded by the National Science Centre, Poland (Grant No UMO-2021/43/B/NZ7/02140).
Results: A nonparametric repeated-measures test showed significant within-respondent differences in acceptance rankings across the eight AI applications (χ²(7)=103.55, p < 0.001). Acceptance (mean±SD) ranged from 5.12±2.61 for remote consultation without medical staff involvement to 6.21±2.16 for genetic/clinical data analysis to guide therapy. Other relatively highly ranked applications included medical image analysis (5.97±2.23) and adherence monitoring (5.93±2.26). The two lowest-ranked applications were remote consultation without medical staff involvement (5.12±2.61) and autonomous robotic surgery (5.37±2.49). Bonferroni-corrected comparisons supported this gradient, especially the contrast between genetic/clinical data analysis and the lowest-ranked applications.
Conclusions: Nurses’ acceptance of AI was not uniform but followed a clear hierarchy, with higher acceptance of data-intensive analytical applications and lower acceptance of autonomous patient-facing or procedural functions. Implementation strategies should differentiate between AI use cases and address concerns related to autonomy, professional oversight, and patient safety.
Key messages: • Nurses distinguish between AI use cases, accepting analytical applications more than autonomous patient-facing or procedural functions.
• Implementation strategies should address autonomy, oversight, and patient safety rather than treating clinical AI as one category.
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BibTeX
@article{smola2026from,
title = {{From data analysis to robotic surgery: nurses’ ranking of autonomous AI in clinical care}},
author = {P Smoła and M Wojcieszko and M Duplaga},
journal = {The European Journal of Public Health},
year = {2026},
month = oct,
doi = {10.1093/eurpub/ckag169.1216},
url = {https://doi.org/10.1093/eurpub/ckag169.1216},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - From data analysis to robotic surgery: nurses’ ranking of autonomous AI in clinical care
AU - P Smoła
AU - M Wojcieszko
AU - M Duplaga
PY - 2026
DA - 2026/10/01
JO - The European Journal of Public Health
DO - 10.1093/eurpub/ckag169.1216
UR - https://doi.org/10.1093/eurpub/ckag169.1216
AB - Background: Artificial intelligence (AI) is entering clinical pathways, yet acceptance varies by clinical task. From a public health perspective, understanding acceptable AI applications is important for safe implementation. This study compared and ranked nurses’ acceptance of different AI applications in clinical practice. Methods: A cross-sectional online survey was conducted among nurses in Poland (N = 800). Quota sampling obtained a large, demographically structured sample. Respondents assessed eight AI applications in diagnostics and treatment on a 1-10 scale, assuming no or minimal involvement of medical staff. Ordinal items were analyzed using the Friedman test, followed by Bonferroni-corrected pairwise comparisons. The study was funded by the National Science Centre, Poland (Grant No UMO-2021/43/B/NZ7/02140). Results: A nonparametric repeated-measures test showed significant within-respondent differences in acceptance rankings across the eight AI applications (χ²(7)=103.55, p < 0.001). Acceptance (mean±SD) ranged from 5.12±2.61 for remote consultation without medical staff involvement to 6.21±2.16 for genetic/clinical data analysis to guide therapy. Other relatively highly ranked applications included medical image analysis (5.97±2.23) and adherence monitoring (5.93±2.26). The two lowest-ranked applications were remote consultation without medical staff involvement (5.12±2.61) and autonomous robotic surgery (5.37±2.49). Bonferroni-corrected comparisons supported this gradient, especially the contrast between genetic/clinical data analysis and the lowest-ranked applications. Conclusions: Nurses’ acceptance of AI was not uniform but followed a clear hierarchy, with higher acceptance of data-intensive analytical applications and lower acceptance of autonomous patient-facing or procedural functions. Implementation strategies should differentiate between AI use cases and address concerns related to autonomy, professional oversight, and patient safety. Key messages: • Nurses distinguish between AI use cases, accepting analytical applications more than autonomous patient-facing or procedural functions. • Implementation strategies should address autonomy, oversight, and patient safety rather than treating clinical AI as one category.
SN - 1101-1262
SN - 1464-360X
ER -