ReviewTranslational pediatrics2026
Applications of artificial intelligence in pediatric general surgery: a systematic review.
Review in Translational pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) technologies are increasingly being applied in the field of pediatric surgery. Utilizing machine learning (ML) to analyze clinical case data, we can develop models for disease diagnosis and prognosis prediction. This study aims to explore whether AI can effectively process massive amounts of medical data, extract key information, and assist doctors in aspects such as disease diagnosis, surgical plan selection, and prognosis assessment. Methods: The protocol of this study was registered with PROSPERO (CRD420251184780). We searched PubMed, Web of Science, and Scopus for studies published between February 2016 and June 2025 focusing on AI applications in pediatric appendicitis, intussusception, Hirschsprung's disease (HD), necrotizing enterocolitis (NEC), and biliary atresia (BA). PRISMA guidelines and Synthesis Without Meta-analysis (SWiM) guidelines were used. Results: Models integrating multimodal data (such as clinical data, laboratory markers, and imaging) generally outperformed those utilizing single data sources. Some models performed at a level comparable to or exceeding that of experienced specialists in diagnosis, improving the diagnostic accuracy of junior physicians. Most included studies were retrospective with single-center designs, resulting in a generally high risk of bias. Conclusions: Current research has demonstrated AI's potential to improve diagnostic accuracy, optimize treatment decisions, and enhance patient outcomes, while improvements are needed in areas such as bias risk control, model interpretability, and data quality. More high-quality, multicenter prospective studies are required to fully realize the comprehensive clinical translation of AI technology in pediatric surgery.
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Registered trials
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 Socratic graph.