Evidence map›Paper›PMID 39917118›Full record

ReviewCureus2025

Role of Artificial Intelligence in the Assessment of Postoperative Pain in the Pediatric Population: A Systematic Review.

Ankit Kasundra, Roshan Chanchlani, Babu Lal, Suresh K Thanveeru, Geetesh Ratre, Reyaz Ahmad, Pramod K Sharma, Amit Agrawal

Abstract readReview
In one paragraph

Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. AI in pediatric surgery: a narrative review.Translational pediatrics · 2026
    Review
  2. Review
  3. 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.

Ankit KasundraPediatric Surgery, All India Institute of Medical Sciences, Bhopal, IND.
Roshan ChanchlaniPediatric Surgery, All India Institute of Medical Sciences, Bhopal, IND.
Babu LalTrauma and Emergency Medicine, All India Institute of Medical Sciences, Bhopal, IND.
Suresh K ThanveeruPediatric Surgery, All India Institute of Medical Sciences, Bhopal, IND.
Geetesh RatrePediatric Surgery, All India Institute of Medical Sciences, Bhopal, IND.
Reyaz AhmadPediatric Surgery, All India Institute of Medical Sciences, Bhopal, IND.
Pramod K SharmaPediatric Surgery, All India Institute of Medical Sciences, Bhopal, IND.
Amit AgrawalNeurosurgery, All India Institute of Medical Sciences, Bhopal, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Effective postoperative pain relief is crucial for the recovery of pediatric patients. While artificial intelligence (AI) is increasingly being applied in pain assessment, there is a notable lack of data regarding its role in managing postoperative pain in children. This systematic review aims to address this gap by focusing on AI's use in predicting and evaluating pediatric postoperative pain. We conducted a comprehensive search of relevant studies from January 2000 to November 2023, identifying 4,491 studies, which were narrowed down to eight based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. These selected studies included 4,470 pediatric patients assessed using various pain measurement tools. The AI models used, primarily deep learning and machine learning, demonstrated accuracy rates ranging from 79% to 85.62% and area under the receiver operating characteristic curve values between 84.00% and 94.00%. Although these AI-based pain assessment tools are still in the early stages, they often focus on single parameters. The heterogeneity of the available publications prevented the conduct of a meta-analysis. Our findings underscore the need for multimodal, multicentric research to improve the performance of AI-based tools for assessing postoperative pain in the pediatric population. Such advancements could significantly enhance the future of pediatric pain management.

Indexed as

artificial intelligencedeep learningmachine learningpediatricpostoperative painpost-surgical pain

Identifiers

PMID39917118
PMCPMC11801105

What OpenQuestion holds

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LicenceCC BY
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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.