ReviewCureus2025
Artificial Intelligence in the Management and Treatment of Pediatric Burns: A Narrative Review of Current Applications and Future Directions.
Review in Cureus, 2025. 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pediatric burn evaluation is often subjective; early estimates of depth and TBSA% drive triage, surgery, and follow-up. Emerging artificial intelligence (AI) tools, especially image-based methods, aim to standardize assessment and extend expertise via telemedicine. To narratively review AI applications relevant to pediatric burn care across diagnosis (depth/healing), TBSA% estimation, treatment planning/monitoring, telemedicine, and rehabilitation, and to summarize practical and ethical considerations for translation. Narrative review of PubMed/MEDLINE and Embase (January 2000-June 2025) with citation chaining. Pediatric and mixed-age studies applicable to pediatric care were included; conference abstracts and preprints were excluded unless subsequently published. Heterogeneity precluded quantitative synthesis; results are qualitatively summarized. Evidence is strongest for image-based assessment (clinical photographs, multispectral/optical modalities) supporting depth/healing classification and TBSA% segmentation, with several studies showing promising discrimination compared with unaided clinical assessment. Telemedicine workflows for remote triage and follow-up appear feasible, while data-driven decision support for timing of surgery and advanced dressing strategies remains investigational. Pediatric-specific datasets, external validation against accepted comparators, and reporting across skin tones and ages are still limited. AI has the potential to complement clinician judgment in pediatric burn care, most immediately for early image-based assessment and remote follow-up. Broader adoption will require pediatric-focused datasets, rigorous external validation, transparent governance for privacy and bias, and clear human-in-the-loop oversight.
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What Socratic holds
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.