ReviewPLOS digital health2025
Personalized decision-making through AI solutions in pediatric emergency medicine: Focusing on febrile children.
Review in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Research on intellectual property rights and legal liability definition of artificial intelligence-assisted pediatric disease diagnosis system.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pediatric emergency medicine (PEM) presents unique challenges due to the diverse developmental stages and medical conditions of young patients. The increasing patient load and nonurgent referrals to pediatric emergency departments (PEDs) emphasize the need for personalized decision-making approaches. These approaches must accommodate the complexities of pediatric care while fostering collaboration between healthcare providers and families. Integrating artificial intelligence (AI) into healthcare settings can transform PEM by enhancing diagnostic accuracy, customizing treatments, and optimizing resource allocation. AI technologies leverage vast datasets, including electronic health records and genetic profiles, to generate personalized diagnostic and treatment plans. Machine learning algorithms can identify patterns in complex data, facilitating early disease detection and precise interventions. This literature review analyzes the role of AI in supporting pediatric emergency care through diagnostic assistance, predictive modeling for febrile disease progression, and outcome optimization. It also highlights the challenges of applying AI in PEM, including data limitations and the need for algorithmic transparency. By addressing these challenges, AI has the potential to revolutionize personalized care in pediatric emergency settings, ultimately improving patient outcomes and care delivery.
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.