Artificial Intelligence and Digital Health in Pediatrics and Neonatology
Artificial Intelligence and Digital Health in Pediatrics and Neonatology explores how algorithmic tools, remote monitoring platforms and digital pathways can augment diagnosis, triage and longitudinal care for children and newborns. This session convenes pediatric clinicians, biomedical engineers, data scientists, informaticians and digital-health implementers to assess AI applications that are clinically validated and ethically deployed. Presentations will cover image-based AI for radiology and dermatology triage, predictive analytics for sepsis and respiratory deterioration in inpatient units, and neonatal vital-sign pattern recognition that flags early decompensation. Digital therapeutics and decision-support integrations—smart checklists embedded in EHRs, automated growth/feeding alerts, and caregiver-facing apps for developmental milestone tracking—will be evaluated for evidence of clinical impact, equity of access and transparent governance.
Implementation workshops will discuss the full product lifecycle: dataset bias mitigation, external validation, regulatory pathways, clinician-in-the-loop deployment, explainability and post-deployment monitoring for safety and drift. Panels will debate pragmatic questions: when AI should advise vs. decide, integration strategies that preserve clinician workflow, and methods to measure outcomes beyond algorithm metrics—reduced time-to-intervention, fewer adverse events and improved caregiver engagement. Sessions will also address digital infrastructure in low-resource settings: low-bandwidth telemedicine, offline-capable decision aids, and privacy-preserving federated learning to include diverse populations in model training. Ethical and legal strands will cover informed consent for data use, pediatric privacy, and governance frameworks to ensure accountability and equitable benefit sharing.
Attendees will receive practical artefacts: data-checklist templates, model-validation playbooks, EHR-integration guides and procurement checklists to evaluate digital-health products for local contexts.
Key Topics Covered
AI for Diagnosis & Early Warning
- Image-based triage, automated measurements and predictive deterioration models
- Validation frameworks and external generalisability testing for pediatric datasets
Digital Therapeutics & Care Pathways
- Caregiver-facing apps, remote monitoring platforms and EHR decision-support tools
- Integration strategies that prioritise clinician workflow and caregiver usability
Equity, Privacy & Governance
- Bias mitigation, federated learning and privacy-preserving architectures
- Consent models for pediatric data and governance frameworks for safe deployment
Implementation & Outcome Measurement
- Post-deployment monitoring, model-drift detection and clinical-impact metrics
- Procurement checklists, procurement and regulatory pathways for digital-health tools
Clinical Impact & Practical Takeaways
Faster, Data-Driven Care
Validated AI models and decision support shorten diagnosis and flag early deterioration.
Improved Access
Low-bandwidth telemedicine and caregiver apps expand reach and continuity of care.
Safer Deployment
Governance checklists and bias mitigation reduce harms and enhance trust in AI systems.
Measurable Outcomes
Focus on clinical impact metrics ensures digital tools deliver real-world benefit, not just accuracy numbers.
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Connect with leading pediatricians, neonatologists, child-health researchers, and multidisciplinary healthcare teams from around the world. Share clinical and translational research and gain practical insights into neonatal intensive care, child development, immunization, nutrition, and integrated strategies to improve outcomes for children.
