ArticleJMIR aging2024
Adoption of Artificial Intelligence-Enabled Robots in Long-Term Care Homes by Health Care Providers: Scoping Review.
Article in JMIR aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence-enabled social robots for facilitating social interactions in patients with dementia: a systematic review.Frontiers in psychology · 2026Pooled it
- Embodied robots in rehabilitation-oriented interventions for older adults with disability or cognitive impairment: a scoping review.Frontiers in rehabilitation sciences · 2026Pooled it
- Factors Influencing the Implementation and Adoption of Digital Nursing Technologies: Systematic Umbrella Review.Journal of medical Internet research · 2025Pooled it
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- Analysis of ICU Nurses' Needs for Intelligent Excreta Nursing Robots: A Multicenter Cross-Sectional Study in China.Healthcare (Basel, Switzerland) · 2026Article
- Service Robots as Work Support for Health Personnel in Long-Term Care: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- Acceptance of Technological Innovations in Emergency Departments: An Empirical Study Based on an Extended TAM.Healthcare (Basel, Switzerland) · 2026Article
- Nurses' Evaluation of a Service Robot for Inpatient Care: Technology Acceptance Study.JMIR nursing · 2026Article
- Family caregivers' acceptance of Artificial Intelligence-enabled technologies for providing care to older adults.BMC geriatrics · 2025Article
- Integrating a Large Language Model Into a Socially Assistive Robot in a Hospital Geriatric Unit: Two-Wave Comparative Study on Performance, Engagement, and User Perceptions.JMIR human factors · 2025Article
- Undergraduate Nursing Students' Perspectives on Artificial Intelligence in Academia.The Canadian journal of nursing research = Revue canadienne de recherche en sciences infirmieres · 2025Article
- Smart Speakers for Health and Well-Being of Older Adults: A Mixed-Methods Review.Healthcare (Basel, Switzerland) · 2025Review
- Factors associated with admission to elderly medical-welfare facilities in South Korea: a cross-sectional machine-learning study.BMJ open · 2025Article
- Artificial Intelligence and Assistive Robotics in Healthcare Services: Applications in Silver Care.International journal of environmental research and public health · 2025Review
- Utilisation of robots in nursing practice: an umbrella review.BMC nursing · 2025Article
- The PDC30 Chatbot-Development of a Psychoeducational Resource on Dementia Caregiving Among Family Caregivers: Mixed Methods Acceptability Study.JMIR aging · 2025Article
- Ethical considerations in the use of social robots for supporting mental health and wellbeing in older adults in long-term care.Frontiers in robotics and AI · 2025Article
- Assessing Visitor Expectations of AI Nursing Robots in Hospital Settings: Cross-Sectional Study Using the Kano Model.JMIR nursing · 2024Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLong-term care (LTC) homes face the challenges of increasing care needs of residents and a shortage of health care providers. Literature suggests that artificial intelligence (AI)-enabled robots may solve such challenges and support person-centered care. There is a dearth of literature exploring the perspectives of health care providers, which are crucial to implementing AI-enabled robots.
objectiveThis scoping review aims to explore this scant body of literature to answer two questions: (1) what barriers do health care providers perceive in adopting AI-enabled robots in LTC homes? (2) What strategies can be taken to overcome these barriers to the adoption of AI-enabled robots in LTC homes?
methodsWe are a team consisting of 3 researchers, 2 health care providers, 2 research trainees, and 1 older adult partner with diverse disciplines in nursing, social work, engineering, and medicine. Referring to the Joanna Briggs Institute methodology, our team searched databases (CINAHL, MEDLINE, PsycINFO, Web of Science, ProQuest, and Google Scholar) for peer-reviewed and gray literature, screened the literature, and extracted the data. We analyzed the data as a team. We compared our findings with the Person-Centered Practice Framework and Consolidated Framework for Implementation Research to further our understanding of the findings.
resultsThis review includes 33 articles that met the inclusion criteria. We identified three barriers to AI-enabled robot adoption: (1) perceived technical complexity and limitation; (2) negative impact, doubted usefulness, and ethical concerns; and (3) resource limitations. Strategies to mitigate these barriers were also explored: (1) accommodate the various needs of residents and health care providers, (2) increase the understanding of the benefits of using robots, (3) review and overcome the safety issues, and (4) boost interest in the use of robots and provide training.
conclusionsPrevious literature suggested using AI-enabled robots to resolve the challenges of increasing care needs and staff shortages in LTC. Yet, our findings show that health care providers might not use robots because of different considerations. The implication is that the voices of health care providers need to be included in using robots. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-doi:10.1136/bmjopen-2023-075278.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.