Evidence map›Paper›PMID 38430433›Full record

ReviewPain and therapy2024

Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review.

Salah N El-Tallawy, Joseph V Pergolizzi, Ingrid Vasiliu-Feltes, Rania S Ahmed, JoAnn K LeQuang, Hamdy N El-Tallawy, Giustino Varrassi, Mohamed S Nagiub

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Pain and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Review
  13. Review
  14. Article
  15. Article
  16. Review
  17. Review
  18. Review
  19. Review
  20. Article
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

8 authors.

Salah N El-TallawyAnesthesia and Pain Department, College of Medicine, King Khalid University Hospital, King Saud University, Riyadh, Saudi Arabia. salaheltallawy@yahoo.com.ORCID http://orcid.org/0000-0001-7328-8840
Joseph V PergolizziNEMA Research, Inc, Naples, FL, USA.
Ingrid Vasiliu-FeltesScience, Entrepreneurship and Investments Institute, University of Miami, Miami, USA.
Rania S AhmedCollege of Medicine, Alfaisal University, Riyadh, Saudi Arabia.ORCID http://orcid.org/0009-0004-1865-1167
JoAnn K LeQuangNEMA Research, Inc., Naples, FL, USA.
Hamdy N El-TallawyNeurology Department, Assiut University, Asyut, Egypt.
Giustino VarrassiPaolo Procacci Foundation (FPP), Rome, Italy.ORCID http://orcid.org/0000-0002-3822-2923
Mohamed S NagiubCollege of Medicine, Alfaisal University, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision-making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions, this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment.

Indexed as

AI in pain assessmentsArtificial intelligence (AI)Computer vision (CV)Machine learning (ML)Natural language processing (NLP)Pain assessmentVirtual reality (VR)Wearable devices and sensors

Identifiers

PMID38430433
PMCPMC11111436

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.