Evidence map›Paper›PMID 38790350›Full record

ReviewBioengineering (Basel, Switzerland)2024

Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review.

Sahar Borna, Michael J Maniaci, Clifton R Haider, Cesar A Gomez-Cabello, Sophia M Pressman, Syed Ali Haider, Bart M Demaerschalk, Jennifer B Cowart, Antonio Jorge Forte

Abstract readReview
In one paragraph

Review in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Caregiver activation: a concept analysis.International journal of nursing studies advances · 2026
    Review
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Advances in AI Technology in Healthcare.Bioengineering (Basel, Switzerland) · 2025
    Article
  17. Observational
  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Sahar BornaDivision of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA.
Michael J ManiaciDivision of Hospital Internal Medicine, Mayo Clinic, Jacksonville, FL 32224, USA.ORCID 0000-0002-2731-1787
Clifton R HaiderDepartment of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA.
Cesar A Gomez-CabelloDivision of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA.
Sophia M PressmanDivision of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA.
Syed Ali HaiderDivision of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA.
Bart M DemaerschalkDepartment of Neurology, Mayo Clinic College of Medicine and Science, Phoenix, AZ 85054, USA.
Jennifer B CowartDivision of Hospital Internal Medicine, Mayo Clinic, Jacksonville, FL 32224, USA.ORCID 0000-0002-7127-7432
Antonio Jorge ForteDivision of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA.ORCID 0000-0003-2004-7538

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI's role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI's role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers' effectiveness and well-being.

Indexed as

ambient intelligenceartificial intelligenceburnoutdigital healthinformal caregivermachine learningquality of life

Identifiers

PMID38790350
PMCPMC11118398

What OpenQuestion holds

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Registered trials

None linked

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.