Evidence map›Paper›PMID 40650728›Full record

SynthesisEuropean journal of pediatrics2025

Leveraging ChatGPT to strengthen pediatric healthcare systems: a systematic review.

Hugo Douma, Courtney McNamara, Maria Bakola, David Stuckler

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in European journal of pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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

4 authors.

Hugo DoumaUniversity of Texas, Austin, TX, USA. hugodouma@utexas.edu.
Courtney McNamaraNewcastle University, England, UK.
Maria BakolaUniversity of Patras, Patras, Greece.
David StucklerBocconi University, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This systematic review is the first to investigate ChatGPT's applications in pediatric healthcare systems by assessing its accuracy and readability, with a focus on its impact across key areas such as clinical decision-making, clinical documentation, patient education, and training. The primary question guiding this review is: How does ChatGPT impact pediatric healthcare systems, for example, in terms of improving patient education, enhancing providers' efficiency, and assisting with clinical decision-making? A systematic review was conducted using PubMed, EMBASE, and Web of Science (February 16, 2025). Inclusion criteria encompassed peer-reviewed studies evaluating ChatGPT in pediatric healthcare (ages 0-18 and guardians). Of 475 screened articles, 58 met eligibility criteria. Two independent reviewers extracted data on study characteristics, intervention types, outcomes, and results. ChatGPT's primary applications were patient education (n = 38), clinical decision-making (n = 12), and clinical documentation (n = 5). Accuracy was highest in patient education, where it generated educational materials and answered FAQs, though readability was often at a high school level, necessitating adaptation. Clinical documentation benefits included improved efficiency in drafting notes and discharge instructions. However, clinical decision-making and training (n = 3) showed mixed accuracy, particularly in management recommendations and patient care plans.

conclusionChatGPT demonstrates potential in enhancing physician efficiency and tailoring patient education in pediatric healthcare. However, most studies relied on observational designs, with only one quasi-experimental study. Further experimental research is required to evaluate AI's impact on pediatric care system effectiveness and patient outcomes. WHAT IS KNOWN: • AI has been increasingly integrated into pediatric healthcare, particularly in imaging, diagnostics, and decision support. • Prior studies have explored LLMs like ChatGPT in medical education and training. WHAT IS NEW: • This is the first systematic review assessing ChatGPT's broader applications in pediatric healthcare, including decision-making, patient education, clinical documentation, and training. • ChatGPT shows high accuracy in patient education and documentation but variable performance in decision-making and training, emphasizing the need for medical supervision.

Indexed as

Clinical Decision-MakingDelivery of Health CarePediatricsAdolescentChildGenerative Artificial IntelligenceHumansPatient Education as TopicChatGPTHealthcareLarge language modelPediatrics

Identifiers

What OpenQuestion holds

Textmetadata
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.