AI am the future: artificial intelligence in pediatric rheumatology

Area of Science:

• Pediatric Rheumatology
• Artificial Intelligence
• Machine Learning

Background:

• Growing interest in AI applications within pediatric rheumatology.
• Ongoing challenges include dataset limitations, ethical concerns, and the need for explainable AI.
• Significant advancements have been made in recent years.

Purpose of the Study:

• To review recent applications of AI in pediatric rheumatology.
• Focus on machine learning models and their outcomes.
• Highlight AI's role in diagnosis, treatment, and patient stratification.

Main Methods:

• Review of recent literature on AI in pediatric rheumatology.
• Focus on machine learning models (supervised and unsupervised).
• Analysis of AI-driven imaging and diagnostic tools.

Main Results:

• Machine learning models identify biomarkers, predict treatment responses, and stratify patients.
• AI-powered imaging and noninvasive methods improve diagnostic accuracy for inflammation and disease activity.
• Large language models show promise for patient-based queries, but human oversight is crucial.

Conclusions:

• AI is revolutionizing pediatric rheumatology, improving diagnosis, classification, and personalized treatment.
• AI enhances patient stratification and disease management strategies.
• The field is rapidly evolving with substantial future potential.