ReviewJournal of perinatology : official journal of the California Perinatal Association2026
Artificial intelligence and digital technologies transforming neonatal respiratory assessment: a scoping review.
Review in Journal of perinatology : official journal of the California Perinatal Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital technologies and artificial intelligence (AI) have been increasingly incorporated into neonatal care, particularly in respiratory assessment. However, structured evidence regarding their clinical applications, performance, and limitations remains limited. This scoping review analyzed 35 studies published between 2019 and 2026 to map the use of digital technologies and AI in neonatal respiratory assessment. The findings showed that these approaches are mainly applied to predictive models, medical image analysis, and continuous physiological monitoring. Overall, the studies demonstrated potential for early diagnosis, prediction of respiratory outcomes, and clinical decision support. Nevertheless, the available evidence remains limited by methodological heterogeneity, lack of robust external validation, small sample sizes, and absence of protocol standardization. These findings suggest that, although promising, AI-based technologies are not yet ready for widespread clinical implementation, requiring prospective, multicenter, and standardized studies to validate their effectiveness and safety in neonatal care. IMPACT: This review synthesizes the use of digital technologies and artificial intelligence in neonatal respiratory assessment, highlighting their expanding role in neonatal respiratory research and clinical decision support. AI-based predictive models demonstrate potential for early identification of critical respiratory outcomes, including bronchopulmonary dysplasia and the need for mechanical ventilation. Imaging-based approaches, such as chest radiography and lung ultrasound combined with deep learning, may improve diagnostic accuracy and reduce interobserver variability. Digital monitoring technologies enable continuous, noninvasive, and real-time assessment of neonatal respiratory parameters. The lack of external validation, methodological standardization, and evidence of clinical impact limits their application, reinforcing the need for multicenter studies and structured integration of these technologies into clinical workflows.
Identifiers
42827094What Socratic 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 Socratic graph.