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Machine Learning in Pediatric Healthcare: Current Trends, Challenges, and Future Directions.

Hammad A Ganatra¹

¹Pediatric Critical Care Medicine, Cleveland Clinic Children's, 9500 Euclid Ave, Cleveland, OH 44195, USA.

Journal of Clinical Medicine

|February 13, 2025

Summary

This summary is machine-generated.

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Artificial intelligence (AI) and machine learning (ML) show promise in pediatric healthcare for diagnosis and treatment. Overcoming data limitations and ethical concerns is crucial for widespread adoption of these advanced technologies.

Area of Science:

Pediatric Healthcare Technology
Computational Medicine
Biomedical Informatics

Background:

Artificial intelligence (AI) and machine learning (ML) are revolutionizing healthcare with predictive and diagnostic capabilities.
Pediatric healthcare faces unique hurdles, including data scarcity, developmental variations, and ethical considerations.

Purpose of the Study:

To review current trends, applications, challenges, and future directions of ML in pediatric healthcare.
To identify research gaps and ethical considerations for advancing ML in pediatrics.

Main Methods:

Systematic literature search of the PubMed database using specific keywords.
Review of selected studies to identify key themes, methodologies, applications, and challenges.
Analysis of research gaps and ethical considerations to propose future research directions.

Keywords:

artificial intelligence machine learning pediatrics

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Main Results:

ML shows potential in pediatric diagnostic support, prognostic modeling, and therapeutic planning.
Applications include early sepsis detection, enhanced diagnostic imaging, and personalized treatments for conditions like epilepsy.
Challenges include data limitations, ethical issues, and model generalizability; federated learning and explainable AI (XAI) are emerging solutions.

Conclusions:

ML holds transformative potential for pediatric healthcare challenges.
Addressing data diversity, ethical guidelines, and model transparency is essential for effective implementation.
Future research should focus on improving data representativeness and ensuring trustworthy AI in pediatric care.