ReviewJournal of perinatology : official journal of the California Perinatal Association2024
Artificial intelligence in the neonatal intensive care unit: the time is now.
Review in Journal of perinatology : official journal of the California Perinatal Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 36 citations in OpenAlex.
- From promise to practice: digital health through the lens of neonatal care.Lancet regional health. Americas · 2026Review
- Ethical aspects of artificial intelligence use in neonatal intensive care units: a scoping review.European journal of pediatrics · 2026Article
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Predicting pulmonary hypertension in infants with bronchopulmonary dysplasia.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- Machine Learning Risk Prediction for Treated Retinopathy of Prematurity in Infants.Neonatology · 2026Article
- Emerging modalities for neuroprognostication in neonatal encephalopathy: harnessing the potential of artificial intelligence.Pediatric research · 2025Review
- Large language models and clinical calculations: to err is human and machines are not exempt.Pediatric research · 2025Article
- Leveraging artificial intelligence for prediction of pulmonary hemorrhage in preterm infants.Journal of perinatology : official journal of the California Perinatal Association · 2025Article
- NeoCLIP: a self-supervised foundation model for the interpretation of neonatal radiographs.NPJ digital medicine · 2025Article
- Review
- Predicting the Need for Cardiopulmonary Resuscitation in Preterm Infants in the Delivery Room Using Machine Learning Models: Analysis of a Korean Neonatal Network Database.Journal of Korean medical science · 2025Article
- Machine learning-based risk prediction models for bronchopulmonary dysplasia in preterm infants: a high-altitude cohort study.BMJ paediatrics open · 2025Article
- Development and Validation of a Novel Deep Learning Model to Predict Pharmacologic Closure of Patent Ductus Arteriosus in Premature Infants.Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography · 2025Article
- AI-guided precision parenteral nutrition for neonatal intensive care units.Nature medicine · 2025Article
- Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning.Pediatric research · 2025Article
- Artificial Intelligence in Midwifery: A Scoping Review of Current Applications, Future Prospects, and Midwives' Perspectives.Healthcare (Basel, Switzerland) · 2025Review
- Explainable Artificial Intelligence in Paediatric: Challenges for the Future.Health science reports · 2024Article
- Combining artificial intelligence and conventional statistics to predict bronchopulmonary dysplasia in very preterm infants using routinely collected clinical variables.Pediatric pulmonology · 2024Article
- Extraction and evaluation of features of preterm patent ductus arteriosus in chest X-ray images using deep learning.Scientific reports · 2024Article
- Development of a machine learning model to identify intraventricular hemorrhage using time-series analysis in preterm infants.Scientific reports · 2024Article
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 at 3 institutions in 1 country.
Funding
Abstract
Artificial intelligence (AI) has the potential to revolutionize the neonatal intensive care unit (NICU) care by leveraging the large-scale, high-dimensional data that are generated by NICU patients. There is an emerging recognition that the confluence of technological progress, commercialization pathways, and rich data sets provides a unique opportunity for AI to make a lasting impact on the NICU. In this perspective article, we discuss four broad categories of AI applications in the NICU: imaging interpretation, prediction modeling of electronic health record data, integration of real-time monitoring data, and documentation and billing. By enhancing decision-making, streamlining processes, and improving patient outcomes, AI holds the potential to transform the quality of care for vulnerable newborns, making the excitement surrounding AI advancements well-founded and the potential for significant positive change stronger than ever before.
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.