Using artificial intelligence to advance pediatric infection prevention and control.

Area of Science:

• Infection Prevention and Control
• Artificial Intelligence Applications
• Pediatric Healthcare

Background:

• Artificial intelligence (AI) shows promise for infection prevention and control (IP&C).
• Most current AI applications in IP&C focus on adult populations.
• Pediatric populations require specific considerations for AI implementation in IP&C.

Purpose of the Study:

• To review the potential of AI in enhancing IP&C for pediatric populations.
• To identify current applications and limitations of AI in pediatric IP&C.
• To explore AI's role in both community and healthcare settings for children.

Main Methods:

• Literature review of AI applications in infection prevention and control.
• Exploration of AI's utility in community and healthcare settings for pediatric populations.
• Analysis of existing data and identification of limitations.

Main Results:

• AI can educate families, detect outbreaks, and prescreen patients in the community.
• AI assists in identifying at-risk patients for healthcare-associated infections (HAIs) and surveillance.
• Limitations include data bias and a lack of prospective validation studies in diverse pediatric groups.

Conclusions:

• AI offers significant potential as a tool for infection recognition and control in pediatric settings.
• Further prospective studies are essential to validate AI tools developed for pediatric IP&C.
• Addressing data biases and heterogeneity in pediatric populations is crucial for effective AI deployment.