Emerging Applications of Artificial Intelligence in Pediatric Care

Clinical Snapshot

Key Findings

• P Value: Not reported at review level

• Effect Size: Not reported at review level; individual study performance metrics referenced descriptively without pooling

• Primary Outcome: Narrative synthesis of AI applications across paediatric acute care, neonatology, neuroimaging, and clinical decision support — no single primary outcome is defined or quantitatively synthesised

• Nnt Or Sensitivity: No pooled sensitivity, specificity, AUC, NNT, or hazard ratio data are presented; individual study statistics are referenced narratively without tabulation

• Confidence Interval: Not reported

Clinical Application

Feasibility varies substantially by application domain. Non-invasive bilirubin estimation via computer vision and ROP screening tools are among the more clinically mature applications. AI-assisted newborn resuscitation guidance and NEC prediction models remain largely in research or early implementation phases. Widespread clinical deployment requires validated, locally calibrated models, robust data governance frameworks, and clinician training. In Australia, AI-based clinical decision support tools in paediatrics are subject to TGA regulation as Software as a Medical Device (SaMD) under the TGA's Digital Health framework. The Australian Digital Health Agency's national strategy supports AI integration but emphasises clinical validation and interoperability with My Health Record. RACGP and RACP have not yet issued specific guidelines on AI use in paediatric practice. PBS implications are indirect — cost savings from reduced diagnostic errors or shorter NICU stays would require formal health technology assessment. Australian NICU networks (e.g., ANZNN) represent potential infrastructure for prospective AI validation studies in neonatal populations. Equity considerations are particularly relevant given the need to ensure AI tools perform equitably across Aboriginal and Torres Strait Islander paediatric populations, who are underrepresented in most training datasets. Paediatric and neonatal patients in tertiary and secondary care settings where AI-enabled clinical decision support, diagnostic imaging analysis, or real-time monitoring tools are being considered for implementation

Abstract

Artificial intelligence (AI) technologies such as machine learning (ML), deep learning (DL), predictive analytics and other tools are rapidly changing pediatric health care, using large amounts of health data. AI tools aid in triage, real-time monitoring and risk stratification in acute care settings, towards improving overall outcomes and fewer complications. During newborn resuscitation, AI analyses real-time data, can guide decisions and enhance training. Computer vision systems with AI tools can generate reliable neonatal bilirubin estimates without the need for blood sampling. AI technology is also being used in the management of necrotising enterocolitis, respiratory distress syndrome, and screening and early diagnosis of retinopathy of prematurity. ML models assist in detecting brain injuries on MRI for conditions such as hypoxic-ischemic encephalopathy, intraventricular hemorrhage; MRI biomarkers can be analyzed using AI to predict neurodevelopmental outcomes. AI-based clinical decision support systems have been deployed to enhance workflows and outcomes by early detection of disease, reducing medication errors and help clinicians improve decision-making. However, there remain ethical and practical challenges in the use of AI including data privacy, the need for high-quality pediatric datasets, rigorous clinical validation and transparency, to ensure that AI strengthens clinical judgement and is trustworthy.

References

1. 1.Murugan, T. P., Ramasamy, S., & Kuruvilla, K. A. (2026). Emerging applications of artificial intelligence in pediatric care. Indian Journal of Pediatrics. https://doi.org/10.1038/s41390-022-02226-1