{
  "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.\n\nMethods: 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).\n\nResults: 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.\n\nConclusions: 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.\n\nKey messages: • Nurses distinguish between AI use cases, accepting analytical applications more than autonomous patient-facing or procedural functions.\n\n• Implementation strategies should address autonomy, oversight, and patient safety rather than treating clinical AI as one category.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "abstract",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "P Smoła"
    },
    {
      "name": "M Wojcieszko"
    },
    {
      "name": "M Duplaga"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7hhjvazg13z1w.bib",
  "cited_by": 0,
  "doi": "10.1093/eurpub/ckag169.1216",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13698195/",
  "id": "7hhjvazg13z1w",
  "issns": [
    "1101-1262",
    "1464-360X"
  ],
  "landing_url": "https://doi.org/10.1093/eurpub/ckag169.1216",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "6. Poster Displays"
    ],
    [
      "heading",
      "AcademicSubjects/MED00860"
    ],
    [
      "heading",
      "AcademicSubjects/SOC01210"
    ],
    [
      "heading",
      "AcademicSubjects/SOC02610"
    ],
    [
      "heading",
      "DE. Digital health and Artificial Intelligence"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7223702940",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13698195/pdf/?tool=EBI",
  "pmcid": "PMC13698195",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/7hhjvazg13z1w.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:07Z",
      "native_id": "PMC13698195",
      "source": "pmc",
      "url": "https://doi.org/10.1093/eurpub/ckag169.1216"
    },
    {
      "first_seen": "2026-10-30T12:21:40Z",
      "native_id": "PMC:PMC13698195",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13698195"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "From data analysis to robotic surgery: nurses’ ranking of autonomous AI in clinical care",
  "title_text": "From data analysis to robotic surgery: nurses’ ranking of autonomous AI in clinical care",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:35:06Z",
  "url": "https://research.draconitic.com/paper/7hhjvazg13z1w",
  "venue": "The European Journal of Public Health",
  "versions": []
}