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TRUVACE RECORD VERSION
record: TRV-2026-1275
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-04T06:55:25.805501Z
status: published
lens: trace
sector: health
headline: Artificial intelligence and digital technologies transforming neonatal respiratory assessment: a scoping review
dek: 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 an…
gain_title: Scoping review of 35 studies found AI and digital tools applied to predictive models, imaging analysis, and continuous monitoring showed potential for early diagnosis and prediction of neonatal respiratory outcomes.
problem_title: Same body of studies was found to be not yet ready for widespread clinical implementation due to lack of robust external validation, small samples, and absence of standardized protocols.
trace_subject: AI and digital technologies for neonatal respiratory assessment
gain_reading: Scoping review of 35 studies found AI and digital tools applied to predictive models, imaging analysis, and continuous monitoring showed potential for early diagnosis and prediction of neonatal respiratory outcomes.
gain_evidence: potential for early diagnosis, prediction of respiratory outcomes, 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
problem_reading: Same body of studies was found to be not yet ready for widespread clinical implementation due to lack of robust external validation, small samples, and absence of standardized protocols.
problem_evidence: AI-based technologies are not yet ready for widespread clinical implementation | lack of external validation, methodological standardization, and evidence of clinical impact limits their application
quick_read: A scoping review published October 2, 2026 analyzed 35 studies from 2019-2026 on digital technologies and AI in neonatal respiratory assessment, finding primary uses in predictive models, medical image analysis, and continuous physiological monitoring with reported potential for early diagnosis and clinical decision support.

The significance is tempered by the review's own assessment that evidence remains heterogeneous and unvalidated, leaving uncertainty about real-world safety, effectiveness, and workflow integration, and indicating that routine clinical adoption would require larger prospective multicenter validation.
limitation: Evidence limited by methodological heterogeneity, lack of robust external validation, small sample sizes, and absence of protocol standardization, with no demonstrated clinical impact yet.
tag: Dual reading
key_points: Review covered 35 studies published between 2019 and 2026 on digital technologies and AI in neonatal respiratory assessment. | Main applications identified were predictive models, medical image analysis including chest radiography and lung ultrasound with deep learning, and continuous physiological monitoring. | Authors noted imaging approaches may improve diagnostic accuracy and reduce interobserver variability, while monitoring enables continuous, noninvasive, real-time assessment.
rundown: The review mapped 35 studies from 2019-2026 and categorized uses into predictive modeling for outcomes like bronchopulmonary dysplasia, deep learning analysis of chest radiography and lung ultrasound, and digital systems for continuous noninvasive monitoring of respiratory parameters.

Despite reported potential for early identification and decision support, authors concluded technologies require prospective, multicenter, standardized studies to validate effectiveness and safety before integration into clinical workflows.
sources:
- peer_reviewed | Journal of Perinatology | https://doi.org/10.1038/s41372-026-02894-5 | 2026-10-02
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