Synthetic data generation in paediatrics and paediatric nursing: what, how, and why?
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View abstract on PubMed
Summary
This summary is machine-generated.Synthetic data (SD) offers benefits for paediatric research, like improving AI models and protecting privacy, but requires careful evaluation and guidelines for safe, effective use in child health.
Area Of Science
- Paediatric Healthcare
- Medical Informatics
- Data Science
Background
- Paediatric research faces data scarcity and privacy challenges.
- Synthetic data (SD) presents a potential solution to these issues.
- The use of SD in paediatrics is an emerging area of study.
Purpose Of The Study
- To explore the benefits and limitations of synthetic data in paediatrics.
- To review current applications and challenges of SD in paediatric research.
- To identify future directions for the development and implementation of SD in child health.
Main Methods
- A narrative literature review was performed.
- Searches were conducted in PubMed and Scopus databases.
- Publications up to August 2025 concerning SD in paediatric healthcare were included.
Main Results
- SD can enhance dataset diversity, protect patient privacy, and aid AI development, particularly for rare diseases.
- Applications span neonatology, oncology, radiology, and neurodevelopmental disorders.
- Challenges include potential bias, quality assurance, privacy risks, and lack of standardized guidelines.
Conclusions
- SD shows promise for paediatric applications like AI early warning systems and augmenting rare disease datasets.
- A structured evaluation framework and paediatric-specific guidelines are necessary.
- Future work should involve multi-stakeholder engagement to ensure fair, safe use and address child development aspects.
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