Care provided by humanoid robots: a scoping review
Bibliographic record
Abstract
Objective: To identify the evidence in the literature regarding the care provided to the population by humanoid robots. Methods: A scoping review based on the guidelines established by the Joanna Briggs Institute. The Preferred Reporting Items for Scoping Review (PRISMA-ScR) checklist was followed. The review protocol was registered on the Open Science Framework under the number osf.io/6ur93. The search was conducted in November 2023 in the following databases: PubMed®, EMBASE®, LILACS, Web of Science, Scopus®, and CINAHL, as well as in the gray literature, including Google Scholar and the Catalog of Theses and Dissertations of the Coordination for the Improvement of Higher Education Personnel (CAPES), using the search strategy: "humanoid robot*" AND "patient*". Results: A total of 27 articles were analyzed. Most of the identified studies were conducted in hospital settings (n=13), with a primary focus on adults (n=10) and children (n=8). The countries with the highest number of publications were Japan (n=6), Canada (n=5), and France (n=4). Three areas of care were identified: social interaction (n=17), physical rehabilitation (n=7), and dissemination of health information (n=3). Additionally, only four studies involved collaboration between humanoid robots and healthcare providers. Conclusion: Despite the increasing use of humanoid robots in healthcare, it remains essential to enhance their integration with professionals in the field. Social interaction highlighted the need to improve patient care, underscoring the importance of aligning the capabilities of these robots with the expertise of healthcare providers. Accordingly, future research should focus on developing strategies that ensure this technology not only assists but also optimizes the quality of care and strengthens interdisciplinary collaboration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".