Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future...

Area of Science:

• Pediatric autoimmune diseases
• Medical informatics
• Machine learning applications

Background:

• Autoimmune disorders impact 4.5%–9.4% of children, affecting quality of life.
• Diagnosis and prognosis are challenging due to varied disease presentation.
• Machine learning (ML) offers pattern recognition from large datasets for improved patient management.

Purpose of the Study:

• To review current knowledge on ML applications in pediatric autoimmune diseases.
• To identify gaps in the existing research and applications of ML.
• To explore the transformative potential of ML in pediatric autoimmune care.

Main Methods:

• Narrative review methodology.
• Extensive literature search across PubMed, Scopus, and Web of Science.
• Focus on ML applications in pediatric autoimmune and related conditions.

Main Results:

• ML algorithms can enhance diagnostic accuracy and speed in pediatric autoimmune disorders.
• ML facilitates the identification of novel biomarkers and therapeutic targets.
• Personalized treatment strategies can be developed using ML-driven analytics.

Conclusions:

• ML holds significant potential to revolutionize the identification, treatment, and management of pediatric autoimmune diseases.
• Physicians can leverage ML for more precise clinical judgments and tailored patient care.
• Further research is needed to fully integrate ML into pediatric autoimmune disease management.