ReviewCureus2025
AI-Driven Neonatal MRI Interpretation: A Systematic Review of Diagnostic Efficiency, Prognostic Value, and Implementation Barriers for Hypoxic-Ischemic Encephalopathy.
Review in Cureus, 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.
- Emerging Applications of Artificial Intelligence in Pediatric Care.Indian journal of pediatrics · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI), especially deep learning techniques, is revolutionizing neonatal neuroimaging by significantly improving the detection and prognostic evaluation of hypoxic-ischemic encephalopathy (HIE), a major contributor to neonatal morbidity and mortality. This systematic review integrates findings from five high-quality, peer-reviewed studies published between 2015 and 2025, identified through comprehensive searches of PubMed, Embase, Scopus, and the Cochrane Library. The review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and applied the Newcastle-Ottawa Scale (NOS), Risk of Bias 2 (RoB 2), and Assessment of Multiple Systematic Reviews 2 (AMSTAR 2) tools to ensure methodological rigor and minimize bias. AI algorithms, especially convolutional neural networks (CNNs), have shown high effectiveness in identifying brain injuries associated with HIE, with sensitivity ranging from 83% to 95% and specificity between 86% and 93%. These models frequently outperform conventional radiological assessments in diagnostic accuracy. These models also reduced interpretation time by up to 47%, streamlining critical care workflows. Prognostic AI tools showed 77-87% accuracy in predicting long-term neurodevelopmental outcomes, aiding in early clinical interventions and family guidance. Despite these promising results, limitations such as small sample sizes (n = 100-200), heterogeneous MRI protocols, and high computational demands hinder broader clinical application. Standardized imaging, multi-center collaboration, and explainable AI models are crucial for clinical scalability. Moreover, successful integration of AI into neonatal intensive care units (NICUs) requires rigorous validation, ethical oversight, and clinician training to ensure safety, transparency, and trust. Collaborative efforts between neonatologists, radiologists, data scientists, and policymakers will be essential to align AI innovations with patient-centered care. As this technology matures, it holds significant potential to improve diagnostic precision, optimize clinical outcomes, and reduce disparities in neonatal neurological 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.