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Artificial intelligence-based clinical decision support in pediatrics.

Sriram Ramgopal1, L Nelson Sanchez-Pinto2,3, Christopher M Horvat4

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Artificial intelligence in clinical decision support (AI-CDS) offers improved accuracy for identifying at-risk children in pediatric care. Challenges remain in data availability and integration, but AI-CDS holds significant future potential.

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Area of Science:

  • Pediatric Healthcare
  • Clinical Decision Support Systems
  • Artificial Intelligence

Background:

  • Machine learning models can enhance clinical decision support (CDS) by identifying children at risk of diagnoses or deterioration.
  • Artificial intelligence-based CDS (AI-CDS) may outperform traditional rule-based systems in pediatric care, offering higher accuracy and fewer errors.
  • Effective AI-CDS requires careful development, transparent rationale, seamless integration, user-friendliness, clinical relevance, provider respect, and scientific rigor.

Purpose of the Study:

  • To review key concepts of AI-CDS in pediatric care.
  • To summarize current applications, potential benefits, and risks of AI-CDS in pediatrics.
  • To highlight the limited but promising integration of AI-CDS in pediatric settings.

Main Methods:

  • Review of existing literature on machine learning and AI-CDS in pediatric care.
  • Analysis of the advantages of AI-CDS over rule-based systems.
  • Identification of challenges and future research directions.

Main Results:

  • AI-CDS models demonstrate potential for increased accuracy and reduced false alerts compared to traditional CDS.
  • Integration of AI-CDS into pediatric care is currently limited.
  • Challenges include lower event rates in children and insufficient large-scale datasets for model development.

Conclusions:

  • AI-CDS has the potential to significantly enhance clinical decision support performance in pediatrics.
  • Further research and development are crucial to overcome current limitations and realize the full benefits of AI-CDS in child healthcare.
  • Addressing data limitations and ensuring proper integration are key for successful AI-CDS implementation.