Artificial intelligence in pediatric healthcare: current applications, potential, and implementation considerations.

Area of Science:

• Pediatric Healthcare Technology
• Clinical Informatics
• Medical Artificial Intelligence

Background:

• Artificial intelligence (AI) is increasingly utilized in healthcare for diagnostics, treatment personalization, and prognosis.
• Pediatric AI applications face unique challenges including developmental variability, limited pediatric data, and ethical considerations like bias and transparency.

Purpose of the Study:

• To provide a narrative review of AI concepts and clinical applications in pediatric care.
• To highlight the role of large language models (LLMs) in administrative tasks, documentation, and decision support.
• To explore AI integration in pediatric surgery, device regulation, and associated safety concerns.

Main Methods:

• Narrative review of existing literature on AI in pediatric healthcare.
• Analysis of AI applications including diagnostics, treatment, prognosis, LLMs, and surgical care.
• Survey of AI-enabled medical devices and relevant regulatory frameworks.

Main Results:

• AI demonstrates potential in diagnostics, personalized treatment, and prognosis for children and adolescents.
• Large language models are emerging for administrative efficiency and clinical decision support.
• Concerns regarding AI reliability, diagnostic safety, and regulatory compliance in pediatric settings persist.

Conclusions:

• AI integration in pediatrics presents both significant opportunities and challenges.
• Pediatricians must adopt AI solutions cautiously and accountably to maximize benefits and mitigate risks.
• Effective collaboration among stakeholders is crucial for responsible AI implementation in pediatric healthcare.