ReviewNPJ digital medicine2023
The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.
Review in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed, 57 citations in OpenAlex.
- From promise to practice: digital health through the lens of neonatal care.Lancet regional health. Americas · 2026Review
- Optimizing Deep Learning for Renal Mass Characterization in Challenging Cases: A Comparative Study of Spatial-Input Strategies and a Validated ROI-Only DLR Nomogram.Annals of surgical oncology · 2026Article
- Ethical aspects of artificial intelligence use in neonatal intensive care units: a scoping review.European journal of pediatrics · 2026Article
- Article
- Automated neonatal sleep positioning assessment by video monitoring and machine learning.Pediatric research · 2026Article
- The effect of an automated patient engagement solutions (APES) on step-by-step parental communication around time of discharge and length of stay.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- Artificial Intelligence in Predicting Mortality Risk for Critically Ill Children in Pediatric Intensive Care Units: A Systematic Review.Health science reports · 2026Review
- A three-factor nomogram predicts the use of invasive mechanical ventilation within 72 h in preterm infants.Frontiers in medicine · 2026Article
- Artificial intelligence and the future of maternal and newborn health in low-income countries: advancing equity, early detection, and health system resilience.Frontiers in public health · 2026Review
- Emerging modalities for neuroprognostication in neonatal encephalopathy: harnessing the potential of artificial intelligence.Pediatric research · 2025Review
- Transforming neonatal care through informatics: A review of artificial intelligence, data, and implementation considerations.Seminars in perinatology · 2025Review
- Leveraging artificial intelligence for prediction of pulmonary hemorrhage in preterm infants.Journal of perinatology : official journal of the California Perinatal Association · 2025Article
- Neonatal pose estimation in the unaltered clinical environment with fusion of RGB, depth and IR images.NPJ digital medicine · 2025Article
- Pediatric pancreas segmentation from MRI scans with deep learning.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2025Article
- Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates.Scientific reports · 2025Article
- Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning.Pediatric research · 2025Article
- Dynamic mortality prediction in critically Ill children during interhospital transports to PICUs using explainable AI.NPJ digital medicine · 2025Article
- A Conceptual Framework for Applying Ethical Principles of AI to Medical Practice.Bioengineering (Basel, Switzerland) · 2025Article
- A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit.NPJ digital medicine · 2025Article
- Ethical considerations regarding the treatment of extremely preterm infants at the limit of viability: a comprehensive review.European journal of pediatrics · 2025Review
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 at 1 institution in 2 countries.
Funding
Abstract
Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.
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.