ReviewWorld journal of pediatric surgery2025
Artificial intelligence and electronic health records: a narrative review of current applications and challenges in pediatric surgery.
Review in World journal of pediatric surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Artificial intelligence and emergency physicians in the USA: enhancing clinical excellence while preserving physicians' judgment.World journal of emergency medicine · 2026Article
- The importance of registries to improve care.Pediatric research · 2026Article
- Outcome strategies for clinical trials in Neuropaediatric rare diseases.Neuroscience applied · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Application of artificial intelligence (AI) within electronic health record (EHR) systems is influencing clinical care, research, education, and quality improvement in pediatric surgical practice. In clinical settings, these technologies organize and structure patient information, enable predictive analytics, and support decision-making tools that enhance awareness and facilitate interventions.Longitudinal datasets in EHR create opportunities for data-driven research ranging from modeling and outcome prediction to clinical trial recruitment. Educational applications of AI-enabled EHR extend to healthcare professionals, trainees, patients, and caregivers. For professionals, case-based learning and simulation resources strengthen decision-making skills and clinical judgment. For patients and caregivers, personalized guidance and accessible health records foster understanding of disease, encourage active participation in care, and improve adherence to management plans. Quality improvement initiatives benefit from AI-assisted audit systems, automated tracking of outcomes, and identification of workflow inefficiencies. These benefits are accompanied by challenges, including the risk of overreliance on automated alerts, limited representativeness of training data, lack of external validation, workflow misalignment, and concerns about privacy and transparency. Addressing these issues through rigorous validation, inclusive datasets, user-focused design, and informatics education will be essential to ensure that AI-enabled EHR systems enhance rather than replace clinician expertise, while promoting safe, equitable, and patient-centered care.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.