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Cited by 91Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIArtificial Intelligence Applications

Nurses’ perceptions, experience and knowledge regarding artificial intelligence: results from a cross-sectional online survey in Germany

Open access
Sommer, Domenic, Schmidbauer, Lukas, Wahl, Florian·2024·BMC Nursing
DOI 10.1186/s12912-024-01884-2PMC PMC10967047
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Abstract

Background: Nursing faces increasing pressure due to changing demographics and a shortage of skilled workers. Artificial intelligence (AI) offers an opportunity to relieve nurses and reduce pressure. The perception of AI by nurses is crucial for successful implementation. Due to a limited research state, our study aims to investigate nurses’ knowledge and perceptions of AI. Methods: In June 2023, we conducted a cross-sectional online survey of nurses in Bavaria, Germany. A convenience sample via care facilities was used for the questionnaire oriented on existing AI surveys. Data analysis was performed descriptively, and we used a template analysis to evaluate free-text answers. Results: 114 (♀67.5 %, ♂32.5 %) nurses participated. Results show that knowledge about AI is limited, as only 25.2 % can be described as AI experts. German nurses strongly associate AI with (i) computers and hardware, (ii) programming-based software, (iii) a database tool, (iv) learning, and (v) making decisions. Two-thirds of nurses report AI as an opportunity. Concerns arise as AI is seen as uncontrollable or threat. Administration staff are seen as the biggest profiteers. Conclusion: Even though there is a lack of clear understanding of AI technology among nurses, the majority recognizes the benefits that AI can bring in terms of relief or support. We suggest that nurses should be better prepared for AI in the future, e.g., through training and continuing education measures. Nurses are the working group that uses AI and are crucial for implementing nursing AI.

AI snapshot

Written from the paper’s full text.

TL;DR

In a June 2023 cross-sectional online survey of 114 nurses in Bavaria, Germany, AI knowledge was limited but attitudes were mostly positive. Most nurses recognized AI as an opportunity, while a minority saw it as a threat or could not judge.

Key findings

  • Only 25.2% of nurses were classified as “AI Connoisseurs/Experts,” while 74.8% lacked a solid AI understanding. Gender differences were observed: 42.9% of men vs. 17.3% of women identified as AI connoisseurs; gender was significantly associated with AI knowledge (p = 0.015, Cramer’s V = 0.335), as were facility type (p = 0.037) and education (p = 0.001).
  • 65.7% perceived AI as an opportunity, 13.7% as a danger/threat, and 21.6% could not judge. Administration and management staff were seen as the biggest profiteers (49.5%), followed by nursing and support staff (25.3%), patients in need of care (17.9%), and social services/support (7.4%).
  • Most known AI application fields were patient monitoring (55.7%), route planning (47.7%), and nursing documentation (43.2% in Table 5; abstract reports 43.7%). Least known were care prediction (38.6%), nursing diagnosis (31.8%), and wound management (21.6%).
  • Qualitative free-text responses associated AI with computers/hardware, programming-based software, data/database tools, learning, and decision-making; concerns included uncontrollability, job replacement, impersonality, errors, and costs; facilitators included data availability, sensitization, refinancing, user-friendliness, and practical use cases.

Methods

  • Cross-sectional online survey using LimeSurvey, conducted June 5–30, 2023, among nurses in Bavaria, Germany. A convenience sample was recruited via emails and multipliers to nursing facilities in Lower Bavaria and Upper Palatinate; 114 nurses completed the survey.
  • The questionnaire was informed by a narrative literature review and existing surveys, especially the BIDT study and Swan et al., and was pre-tested with 6 nurses. It covered demographics, AI knowledge, perceptions, and open-ended free-text items.
  • Quantitative data were analyzed descriptively in IBM SPSS Version 29 using frequencies and percentages, plus bivariate statistics including Spearman’s rank correlation, Cramer’s V, cross-tabulation, and chi-square tests. Free-text answers were analyzed with a structured qualitative template analysis according to Mayring.

Limitations

  • The paper states that “perception” was not clearly defined; convenience sampling and the Bavaria focus may not reflect the broader nursing population; the small sample of 114 limits statistical power and multivariate analysis, with expected counts under 5 in 80% of cells; reliance on existing instruments and omission of TAM/UTAUT may have limited theoretical depth; survey brevity may have under-assessed demographics and AI experiences; inclusion/exclusion focused on formal nurses only; and the cross-sectional design may not capture evolving perceptions.
  • The paper also notes sampling bias and non-validated items, and states that low cell counts mean bivariate results should be interpreted cautiously and require further research with larger samples.
  • Evident from the text: findings come from one German region and a convenience sample, so generalization to all nurses or other healthcare professions is limited.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1186/s12912-024-01884-2

Cites

37 of 37 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 39 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Age was not a significant factor for AI knowledge.

    “At the same time, age was not a significant factor.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Nurses' definitions of AI offer a multidimensional understanding.

    “In addition, study participants’ definitions of AI offer a multidimensional understanding that extends beyond the quantitative assumptions”
    Quote in the paperNot a substantial claim
  • Findingown data

    Two-thirds of nurses report AI as an opportunity.

    “Two-thirds of nurses report AI as an opportunity.”
    Quote in the paperSubstantial claim
  • Findingnone given

    Administration staff are seen as the biggest profiteers.

    “Administration staff are seen as the biggest profiteers.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Participants often see AI as a tool that simulates human interactions or replicates human-like behaviour.

    “Participants often see AI as a tool that simulates human interactions or replicates human-like behaviour.”
    Quote in the paperNot a substantial claim
  • Classified as probably not a substantial claim. 42
  • May combine two claims. 37
  • The quote only partly states this claim. 1

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Most nurses (29.2%) were 31 to 40 years old.

    “most nurses (29.2 %) were 31 to 40 years old.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    AI has potential in housekeeping activities through AI-based cleaning robots.

    “Furthermore, AI has potential in ‘housekeeping activities’ (Text 2, para. 134) through AI-based cleaning robots.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Type of healthcare facility and educational level showed significant influences on AI knowledge.

    “Additionally, the type of healthcare facility ( value = 22.078, p = 0.037, Cramer’s V = 0.257) and educational level ( value = 46.063, p = 0,001, Spearman’s correlation = 0.279) also showed significant influences on AI knowledge.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Nurses recognize that AI is not a ready-to-use solution but requires data processing and preparation.

    “Furthermore, nurses recognize that AI is not a ready-to-use solution but requires data processing and preparation (Text 1, para. 11).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Nurses know least about care prediction (38.6%), nursing diagnosis (31.8%), and wound management (21.6%).

    “Nurses know least about the AI areas of care prediction (38.6 %), nursing diagnosis (31.8 %), and wound management (21.6 %).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Nurses name AI providers such as OpenAI.

    “Nurses also name AI providers, such as OpenAI (Text 1, para. 48).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    AI-supported technical assistance like active exoskeletons were highlighted.

    “AI-supported technical assistance, like active exoskeletons, were also highlighted (Text 2, para. 128).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Nurses associate AI with computers/hardware, software, human-like characteristics, data-driven tool, learning, and decision-making.

    “The qualitative analysis reveals that nurses associate AI with (i) computers and hardware, (ii) software, (iii) human-like characteristics exemplified by natural language processing, (iv) a data-driven tool capable of (v) learning, and (vi) decision-making.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Nurses are most familiar with patient monitoring (55.7%), route planning (47.7%), and AI-aided nursing documentation (43.7%).

    “For the fields of application of AI in nursing (Table 5), nurses are most familiar with patient monitoring (55.7 %) and route planning (47.7 %) as well as AI-aided nursing documentation (43.7 %).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    AI is seen as a dynamic tool with the potential to learn and expand its capabilities.

    “AI is seen as a dynamic tool with the potential to learn and expand its capabilities (Text 1, para 55).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The qualitative template analysis revealed four main categories: knowledge and definition of AI, opportunities and perceived benefits, anxiety and disadvantages, and facilitators and future needs.

    “The analysis revealed four main categories. These are (i) Knowledge and definition of AI, (ii) Opportunities and perceived benefits, (iii) Anxiety and disadvantages, and (iv) Facilitators and future needs.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). The quote only partly states this claim.

    Findingown data

    Two-thirds of participants were female (67.5%).

    “As Table 3 shows, two-thirds of our participants were female”
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    Findingown data

    The study population has high education levels: 31.0% university degrees and 29.2% completed apprenticeships.

    “Moreover, our study population has high education levels, like university degrees (31.0 %) and completed apprenticeships (29.2 %).”
    Quote in the paperNot a substantial claim

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    Findingown data

    Qualitative data reveals AI's utility extends to patient monitoring, routine nursing tasks, drug compatibility, patient care, and intelligent incontinence care.

    “Our qualitative data reveals AI’s utility extends to patient monitoring, routine nursing tasks, assessing ‘drug compatibility effects and interactions’ (Text 2, para. 124), enhancing ‘patient care’ (Text 2, para. 127), and assisting with ‘intelligent incontinence care’ (Text 2, para. 130).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    AI is associated with robots and machines, and is frequently integrated as software into robotic systems.

    “This view is further influenced by AI’s association with robots and machines, recognizing that AI is frequently integrated as software into robotic systems.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    39.8% of nurses work in inpatient long-term care, 18.6% in hospitals, 15.9% in outpatient care, and 25.7% in other care settings.

    “Regarding the form of the facilities, 39.8 % of nurses work in inpatient long-term care, followed by hospitals (18.6 %) and outpatient care (15.9 %). One-quarter work in other care settings, like specialized care.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Respondents expressed that AI can be a valuable relief in nursing, aiding everyday activities, optimizing work processes, and supporting data collection and interpretation.

    “respondents expressed that AI can be a valuable relief in nursing, aiding everyday activities, optimizing work processes, and supporting data collection and interpretation, such as analyzing feelings or vital parameters.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    AI's predictive capabilities are beneficial, allowing for early detection of care needs and risks, such as sepsis from care and laboratory parameters.

    “AI’s predictive capabilities are beneficial, allowing for early detection of care needs and risks, such as sepsis from care and laboratory parameters”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Findingown data

    User experience is a critical factor; AI solutions must be user-friendly, easy to use, and time-saving.

    “user experience is another critical factor. AI solutions must be user-friendly, easy to use, and time-saving”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    The advantages of AI for these parties include more efficient processes such as invoice verification for insurers, early risk detection to contain healthcare costs, staffing savings for operators and employers, predictive care needs and assessment development in nursing science, and support for social participation in social services.

    “The advantages include more efficient processes such as invoice verification for insurers, early risk detection to contain healthcare costs, staffing savings for operators and employers, predictive care needs and assessment development in nursing science, and support for social participation in social services”
    Quote in the paperNot a substantial claim

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    Findingown data

    Other concerns include losing control over AI, which can cause feelings of helplessness, and the high costs associated with AI implementation and maintenance.

    “Other concerns include losing control over AI, which can cause feelings of helplessness and the high costs associated with AI implementation and maintenance”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Only 25.2% of surveyed nurses have substantial AI knowledge, while the majority (73.0%) have limited understanding; 74.8% lack a solid AI understanding.

    “our study found that only 25.2 % of nurses have substantial AI knowledge, with the majority (73.0 %) having limited understanding.”
    • Also stated in: § Nurses AI perception takeaways · plain
      “In summary, 74.8 % of our surveyed nurses lack a solid AI understanding.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Nurses identified several parties who may profit from AI through qualitative data: micro level (physicians and hospital operators), meso level (family nurses, social services, and service personnel), and macro level (health and care insurers, government and policymakers, and nursing scientists).

    “In addition, nurses identified several parties who may profit from AI through qualitative data: (i) Micro Level - Physicians and hospital operators, (ii) Meso Level - Family nurses, social services, and service personnel, and (iii) Macro Level- Health and care insurers, government and policymakers, and nursing scientists.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Nurses understand that nursing is fundamentally grounded in social relationships between nurses and patients, a dimension that AI might render impersonal.

    “This is coupled with the understanding that nursing is fundamentally grounded in social relationships between nurses and patients, a dimension that AI might render impersonal.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Findingreasoning

    AI solutions are still not satisfying enough, leading to the potential for improvements.

    “the quotes show that AI solutions are still not satisfying enough, leading to the potential for improvements.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Qualitative data show an optimistic view of AI's potential, recognizing its ability to support and transform nursing.

    “The qualitative data confirm an optimistic view of AI’s potential, recognizing its ability to support and transform nursing.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Nearly half of the nurses think that administration and management staff (49.5%) can be supported most by AI, followed by nursing and support staff (25.3%), patients in need of care (17.9%), and social services and support (7.4%).

    “Nearly half of the nurses think that administration and management staff (49.5 %) can be supported most by AI, followed by nursing and support staff (25.3 %), patients in need of care (17.9 %), and social services and support (7.4 %).”
    • Also stated in: § Nurses AI perception takeaways · plain
      “Beneficial user groups are mostly administration and management staff (49.5 %) as well as nurses and their support staff (25.3 %).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Findingown data

    Human involvement is essential in shaping AI, meaning active participation in its design and regulation, maintaining patient relationships, and fostering trust through sensitization and education about AI.

    “In addition to data considerations, human involvement is essential in shaping AI. This means active participation in its design and regulation, maintaining patient relationships, and fostering trust through sensitization and education about AI”
    Quote in the paperNot a substantial claim

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    Findingown data

    The ability of AI to enhance human-computer interaction is recognized, particularly through AI-based voice control capabilities.

    “The ability of AI to enhance human-computer interaction is also recognized, particularly through AI-based voice control capabilities.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The most known AI applications are patient monitoring (55.7%), route planning (47.7%), and nursing documentation (43.2%).

    “For RQ 2 the most known AI applications are (i) patient monitoring (55.7 %), (ii) route planning (47.7 %), and (iii) nursing documentation (43.2 %).”
    Quote in the paperNot a substantial claim

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    Findingown data

    Open-ended responses reveal a need for regulation, accessibility, and a focus on alleviating nurses' workloads.

    “Additionally, open-ended responses reveal a need for regulation, accessibility, and a focus on alleviating nurses’ workloads.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    AI offers an opportunity to relieve nurses and reduce pressure.

    “Consequently, AI-supported solutions are emerging as key responses to these pressures [8, 10].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [8] Reiss D, Spina A. Scaling smart solutions with AI in health: Unlocking impact on high potential use cases. 2023. https://www3.weforum.org/docs/WEF_Scaling_Smart_Solutions_with_AI_in_Health_Unlocking_Impact_on_High_Potential_Use_Cases.pdf. Accessed 25 Mar 2024.

      Not checked in the free run.

    • cites [10] Watson D, Womack J, Papadakos S. Rise of the robots: Is artificial intelligence a friend or foe to nursing practice? Crit Care Nurs Q. 2020;43(3):303–11. source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Nursing faces increasing pressure due to changing demographics and a shortage of skilled workers.

    “European healthcare, challenged by an aging population and nursing staff shortages, faces increasing demands and work-related stress [9, 10].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [9] Huhtala M, Geurts S, Mauno S, Feldt T. Intensified job demands in healthcare and their consequences for employee well-being and patient satisfaction: A multilevel approach. J Adv Nurs. 2021;77(9):3718–32. https://doi.org/10.1111/jan.14861. source

      Not checked in the free run.

    • cites [10] Watson D, Womack J, Papadakos S. Rise of the robots: Is artificial intelligence a friend or foe to nursing practice? Crit Care Nurs Q. 2020;43(3):303–11. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    Even though there is a lack of clear understanding of AI technology among nurses, the majority recognizes the benefits that AI can bring in terms of relief or support.

    “Even though there is a lack of clear understanding of AI technology among nurses, the majority recognizes the benefits that AI can bring in terms of relief or support.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Concerns include unawareness, mistrust, anxiety, worries about the patient relationship and job replacement.

    “Concerns include unawareness, mistrust, anxiety, worries about the patient relationship and job replacement [5, 8, 10, 13, 25–27].”
    Quote in the paperNot a substantial claim0 of 7 sources checked
    • cites [5] Castagno S, Khalifa M. Perceptions of Artificial Intelligence Among Healthcare Staff: A Qualitative Survey Study. Front Artif Intell. 2020;3:578983. https://doi.org/10.3389/frai.2020.578983. source

      Not checked in the free run.

    • cites [8] Reiss D, Spina A. Scaling smart solutions with AI in health: Unlocking impact on high potential use cases. 2023. https://www3.weforum.org/docs/WEF_Scaling_Smart_Solutions_with_AI_in_Health_Unlocking_Impact_on_High_Potential_Use_Cases.pdf. Accessed 25 Mar 2024.

      Not checked in the free run.

    • cites [10] Watson D, Womack J, Papadakos S. Rise of the robots: Is artificial intelligence a friend or foe to nursing practice? Crit Care Nurs Q. 2020;43(3):303–11. source

      Not checked in the free run.

    • cites [13] Buchanan C, Howitt ML, Wilson R, Booth RG, Risling T, Bamford M. Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review. JMIR Nurs. 2020;3(1):e23939. https://doi.org/10.2196/23939. source

      Not checked in the free run.

    • cites [25] Kwak Y, Ahn JW, Seo YH. Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions. BMC Nurs. 2022;21(1):267. https://doi.org/10.1186/s12912-022-01048-0. source

      Not checked in the free run.

    • cites [26] Swan BA. Assessing the Knowledge and Attitudes of Registered Nurses about Artificial Intelligence in Nursing and Health Care. Nurs Econ. 2021;39(3):139–43.

      Not checked in the free run.

    • cites [27] Chew HSJ, Achananuparp P. Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review. J Med Internet Res. 2022;24(1):e32939. https://doi.org/10.2196/32939. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    Questions about nurses' prior experiences with AI in their work were based on dimensions resulting from a scoping review.

    “Questions about nurses’ prior experiences with AI in their work were based on dimensions resulting from a scoping review [35].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [35] Zi Qi P, Li Ying JL, Han Shi JC, Ying L. The role of artificial intelligence in enhancing clinical nursing care: A scoping review. J Nurs Manag. 2022;30(8):3654–74. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    The more accessible and trustworthy the data, the more robust the decisions the AI system makes.

    “The more accessible and trustworthy the data, the more robust the decisions the AI system makes.”
    Quote in the paperSubstantial claim

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    Interpretationreasoning

    Without such data, AI outcomes may be unsatisfactory.

    “Without such data, AI outcomes may be unsatisfactory”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Over 70% of nurses agree that AI can catalyze transformation in nursing through better health promotion, personalized treatments, and automation in administration as well as routine tasks.

    “Over 70 % of nurses agree that AI can catalyze transformation in nursing through better health promotion, personalized treatments, and automation in administration as well as routine tasks [26].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [26] Swan BA. Assessing the Knowledge and Attitudes of Registered Nurses about Artificial Intelligence in Nursing and Health Care. Nurs Econ. 2021;39(3):139–43.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationreasoning

    The inability of 21.6% of respondents to categorize AI indicates they could not judge due to a lack of knowledge.

    “indicating that they could not judge due to a lack of knowledge.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    Participants consented voluntarily and anonymously, with the study adhering to ethical guidelines.

    “Participants consented voluntarily and anonymously, with the study adhering to ethical guidelines.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    A pre-test involving 6 nurses ensured the survey's clarity and reliability.

    “A pre-test involving 6 nurses ensured the survey’s clarity and reliability, leading to minor adjustments in wording.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductown data

    Due to low cell counts in tests, results should be interpreted cautiously, indicating the need for further research with larger samples.

    “Due to low cell counts in our tests, these results should be interpreted cautiously, indicating the need for further research with larger samples.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    Due to limited sample size, results should be considered indicative, leading to the need for further research.

    “However, due to our study’s limited sample size, these results should be considered indicative, leading to the need of further research [36].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [36] Bewick V, Cheek L, Ball J. Statistics review 8: Qualitative data - tests of association. Crit Care. 2004;8(1):46. https://doi.org/10.1186/cc2428. source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In Germany, two-thirds of the public don't understand AI.

    “In Germany, two-thirds of the public don’t understand AI [22].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [22] Stürz R, Stumpf C, Mendel U. Bayerisches Forschungsinstitut für Digitale Transformation, editor. Künstliche Intelligenz verstehen und gestalten: Ergebnisse und Implikationen einer bidt-Kurzbefragung in Deutschland: Analysen und Studien. Berlin; 2020. https://www.bidt.digital/wp-content/uploads/sites/2/2022/08/bidt_KI-Studie_Juni-2020.pdf. Accessed 25 Mar 2024.

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Integrating AI is hindered by fears, missing sensitization, and a lack of data.

    “Integrating AI is hindered by fears, missing sensitization, and a lack of data [8, 22, 23, 25, 27, 30].”
    Quote in the paperSubstantial claim0 of 6 sources checked
    • cites [8] Reiss D, Spina A. Scaling smart solutions with AI in health: Unlocking impact on high potential use cases. 2023. https://www3.weforum.org/docs/WEF_Scaling_Smart_Solutions_with_AI_in_Health_Unlocking_Impact_on_High_Potential_Use_Cases.pdf. Accessed 25 Mar 2024.

      Not checked in the free run.

    • cites [22] Stürz R, Stumpf C, Mendel U. Bayerisches Forschungsinstitut für Digitale Transformation, editor. Künstliche Intelligenz verstehen und gestalten: Ergebnisse und Implikationen einer bidt-Kurzbefragung in Deutschland: Analysen und Studien. Berlin; 2020. https://www.bidt.digital/wp-content/uploads/sites/2/2022/08/bidt_KI-Studie_Juni-2020.pdf. Accessed 25 Mar 2024.

      Not checked in the free run.

    • cites [23] Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, et al. Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review. J Med Internet Res. 2021;23(11):e26522. https://doi.org/10.2196/26522. source

      Not checked in the free run.

    • cites [25] Kwak Y, Ahn JW, Seo YH. Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions. BMC Nurs. 2022;21(1):267. https://doi.org/10.1186/s12912-022-01048-0. source

      Not checked in the free run.

    • cites [27] Chew HSJ, Achananuparp P. Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review. J Med Internet Res. 2022;24(1):e32939. https://doi.org/10.2196/32939. source

      Not checked in the free run.

    • cites [30] Abdullah R, Fakieh B. Health Care Employees’ Perceptions of the Use of Artificial Intelligence Applications: Survey Study. J Med Internet Res. 2020;22(5):e17620. https://doi.org/10.2196/17620. source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationown data

    Our results indicate that administrative and routine tasks are prime areas for AI, making them strategic starting points for maximizing AI's potential.

    “Our results indicate that administrative and routine tasks are prime areas for AI, making them strategic starting points for maximizing AI’s potential.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductreasoning

    The inclusion and exclusion criteria are specific, focusing on formal nurses; the study did not consider other healthcare professionals, missing comparative insights.

    “Furthermore, the inclusion and exclusion criteria are specific, focusing on formal nurses. Our study did not consider other healthcare professionals, missing out on comparative insights of AI perception across healthcare roles.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

    Interpretationcitation

    With the mounting pressure on nursing, AI is poised to play a vital role in alleviating healthcare staff and meeting the escalating nursing demands in Western Europe.

    “With the mounting pressure on nursing, AI is poised to play a vital role in alleviating healthcare staff and meeting the escalating nursing demands in Western Europe [10, 23].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [10] Watson D, Womack J, Papadakos S. Rise of the robots: Is artificial intelligence a friend or foe to nursing practice? Crit Care Nurs Q. 2020;43(3):303–11. source

      Not checked in the free run.

    • cites [23] Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, et al. Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review. J Med Internet Res. 2021;23(11):e26522. https://doi.org/10.2196/26522. source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductreasoning

    The small study sample constraint hinders the ability to explore interrelations and generalizations are limited.

    “In summary, the small study sample constraint hinders our ability to explore interrelations and generalizations are limited.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The study's reliance on existing instruments like the BIDT study, while omitting broader models such as TAM or UTAUT, may have affected the depth of theoretical exploration.

    “Additionally, the study’s reliance on existing instruments like the BIDT study [22], while omitting broader models such as TAM or UTAUT may have affected the depth of theoretical exploration.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [22] Stürz R, Stumpf C, Mendel U. Bayerisches Forschungsinstitut für Digitale Transformation, editor. Künstliche Intelligenz verstehen und gestalten: Ergebnisse und Implikationen einer bidt-Kurzbefragung in Deutschland: Analysen und Studien. Berlin; 2020. https://www.bidt.digital/wp-content/uploads/sites/2/2022/08/bidt_KI-Studie_Juni-2020.pdf. Accessed 25 Mar 2024.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    The cross-sectional design might not capture the evolving perceptions of AI.

    “Our cross-sectional design might not capture the evolving perceptions of AI, especially in the context of rapidly changing media narratives.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The survey's brevity could have resulted in an under-assessment of detailed nurse demographics and AI experiences.

    “The survey’s brevity could have resulted in an under-assessment of detailed nurse demographics and AI experiences.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Although many nursing staff know too little about AI, two-thirds view AI optimistically, suggesting openness to AI implementation despite concerns about patient relationships, errors, costs, and job security.

    “Although many nursing staff know too little about AI, two-thirds view AI optimistically, suggesting openness to AI implementation despite concerns about patient relationships, errors, costs, and job security.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Current research focuses on AI solutions for patient monitoring and clinical decision support.

    “Current research, including works like Cho et al. [41], focuses on AI solutions for patient monitoring and clinical decision support.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [41] Cho I, Cho J, Hong JH, Choe WS, Shin H. Utilizing standardized nursing terminologies in implementing an AI-powered fall-prevention tool to improve patient outcomes: a multihospital study. J Am Med Inform Assoc. 2023;30(11):1826–36. https://doi.org/10.1093/jamia/ocad145. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The concept of 'perception' itself is not clearly defined, potentially encompassing aspects of usage and acceptance.

    “First, the concept of ‘perception’ itself is not clearly defined, potentially encompassing aspects of usage and acceptance, which visualizes the need for ground research.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationown data

    This finding underscores the evolving roles in AI-integrated healthcare and aligns with Swan et al. [26].

    “This finding underscores the evolving roles in AI-integrated healthcare and aligns with Swan et al. [26].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Swan et al. [26] Swan BA. Assessing the Knowledge and Attitudes of Registered Nurses about Artificial Intelligence in Nursing and Health Care. Nurs Econ. 2021;39(3):139–43.

      Not checked in the free run.

  • May combine two claims.

    Study design and conductreasoning

    The convenience sampling approach and focus on Bavaria may not accurately reflect the broader nursing population, potentially introducing bias.

    “Our convenience sampling approach and the focus on Bavaria may not accurately reflect the broader nursing population, potentially introducing bias.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    Nurses often perceive AI technically, which might contribute to skepticism and hinder acceptance.

    “Nurses often perceive AI technically, which might contribute to skepticism and hinder acceptance.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Utilizing AI to streamline administrative processes can reduce bureaucratic workload.

    “Utilizing AI to streamline these processes can reduce bureaucratic workload.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Time studies in nursing highlight the considerable time spent on routine and administrative tasks, thereby supporting the use of AI in areas like monitoring and documentation.

    “Time studies in nursing highlight the considerable time spent on routine and administrative tasks, thereby supporting the use of AI in areas like monitoring and documentation [39, 40].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [39] Westbrook JI, Duffield C, Li L, Creswick NJ. How much time do nurses have for patients? a longitudinal study quantifying hospital nurses’ patterns of task time distribution and interactions with health professionals. BMC Health Serv Res. 2011;11(1). https://doi.org/10.1186/1472-6963-11-319. source

      Not checked in the free run.

    • cites [40] Roche MA, Friedman S, Duffield C, Twigg DE, Cook R. A comparison of nursing tasks undertaken by regulated nurses and nursing support workers: a work sampling study. J Adv Nurs. 2017;73(6):1421–32. https://doi.org/10.1111/jan.13224. source

      Not checked in the free run.

61 of 61

  • The paper relies on this without support.

    The convenience sample of 114 nurses in Bavaria is representative of German nurses.

    “German nurses strongly associate AI with (i) computers and hardware, (ii) programming-based software, (iii) a database tool, (iv) learning, and (v) making decisions.”

  • The paper relies on this without support.

    Self-reported AI knowledge accurately reflects actual AI knowledge.

    “Table 4 shows nurses’ self-assessment of AI knowledge, measured by five items.”

  • The authors state this as an assumption.

    The use of AI in nursing care in Bavaria has so far made little inroads and nurses have hardly been confronted with AI in the working context.

    “Nevertheless, open-ended data and the lack of knowledge let us assume that the use of AI in nursing care in Bavaria has so far made little inroads and that nurses have, therefore, hardly been confronted with AI in the working context.”
3 of 3

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • AI-knowledge counts do not match Table 4's stated N

    Minor

    The five response counts total 111, not the stated N=113. The printed percentages correspond to 111 responses, indicating that the table's stated denominator is wrong or two responses are unaccounted for.

    Reported 113 · recomputed 111

  • Nursing-documentation percentage differs from Table 5

    Minor

    The results text gives 43.7% for nursing documentation, while Table 5 gives 43.2%.

  • Education counts do not match Table 3's stated N

    Minor

    The five education categories total 111 participants, but the table states N=113 except for gender. The table does not account for the other two participants.

    Reported 113 · recomputed 111

3 of 3

Cites

Flagged first, then by citations

2 of the 39 references couldn’t be matched to a known paper.

Research works printed but not linked: 3

  1. Linz K, Stula S. Demographic change in Europe-an overview. Obs Sociopolitical Developements Eur. 2010;4(1):2–10.
  2. Swan BA. Assessing the Knowledge and Attitudes of Registered Nurses about Artificial Intelligence in Nursing and Health Care. Nurs Econ. 2021;39(3):139–43.
  3. Zi Qi P, Li Ying JL, Han Shi JC, Ying L. The role of artificial intelligence in enhancing clinical nursing care: A scoping review. J Nurs Manag. 2022;30(8):3654–74. · DOI · Paper page

Cited by

Sorted by citations

Related

Authors
Sommer, D., Schmidbauer, L., & Wahl, F.
Affiliations
Deggendorf Institute of Technology (DE)
Corresponding author
Sommer, Domenic
ORCID
Venue
BMC Nursing
Publisher
Springer Science and Business Media LLC
Published
2024-03-27
Volume
23
Issue
1
Pages
205-205
ISSN
1472-6955 (ISSN-L)
DOI
10.1186/s12912-024-01884-2
PMC
PMC10967047
OpenAlex
W4393228197
Semantic Scholar
92767638b7e39290c030e9a20df4b1e7ba9ce14a
Cited by
91
References
39
Citations by year
2026: 41 · 2025: 38 · 2024: 11 · 2022: 1
FWCI
8.41
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Nursing (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PUBLISSO (German National Library of Medicine) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Opus4 THD (Technische Hochschule Deggendorf (THD)) (submittedVersion, cc-by)
Funders
  • Hightech Agenda Bavaria
  • THD - Technische Hochschule Deggendorf
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
artificial intelligence, nurses' perceptions, nursing shortage, nursing education, nursing facilities, Bavaria

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.

Statistics and arithmetic are recomputed, not judged.

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