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Pediatric Predictive Artificial Intelligence Implemented in Clinical Practice from 2010 to 2021: A Systematic Review.

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Area of Science:

  • Pediatric artificial intelligence
  • Clinical informatics
  • Healthcare technology assessment

Background:

  • Artificial intelligence (AI) is increasingly developed for pediatric applications.
  • However, the actual implementation and impact of data-driven AI in pediatric clinical settings remain underexplored.
  • Existing studies often lack standardized outcome reporting.

Purpose of the Study:

  • To systematically review and analyze pediatric artificial intelligence (AI) implementation studies published between 2010 and 2021.
  • To evaluate the reported clinical outcomes, process measures, and human performance metrics of these AI interventions.
  • To identify gaps in the evaluation of AI in pediatric healthcare.

Main Methods:

  • A comprehensive literature search was conducted across multiple databases (PubMed/Medline, Embase, CINHAL, Cochrane, IEEE, Web of Science).
  • Inclusion criteria focused on data-driven AI interventions providing patient-specific recommendations in pediatric clinical settings with demonstrable agency.
  • Data extracted included study characteristics, implementation details, and performance measures.

Main Results:

  • Out of 126 full-text articles reviewed, only 17 met the inclusion criteria for AI implementation studies.
  • While 71% of studies reported improvements in process measures, only 48% showed improvements or positive effects on clinical outcomes.
  • Five studies reported no difference in clinical outcomes, and one reported poor outcomes; standardized reporting was lacking across most studies.

Conclusions:

  • The number of published pediatric AI models significantly exceeds the number of actual clinical implementations.
  • There is a critical need for standardized reporting of clinical outcomes, process measures, and human performance to accurately assess AI's impact in pediatrics.
  • Further comprehensive evaluations are necessary to understand the mechanisms through which AI influences pediatric care.