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

Objective: To review pediatric artificial intelligence (AI) implementation studies from 2010-2021 and analyze reported performance measures.

Methods: We searched PubMed/Medline, Embase CINHAL, Cochrane Library CENTRAL, IEEE and Web of Science with controlled vocabulary.

Inclusion criteria: AI intervention in a pediatric clinical setting that learns from data (i.e., data-driven, as opposed to rule-based) and takes actions to make patient-specific recommendations; published between 01/2010 to 10/2021; must have agency (AI must provide guidance that affects clinical care, not merely running in background). We extracted study characteristics, target users, implementation setting, time span, and performance measures.

Results: Of 126 articles reviewed as full text, 17 met inclusion criteria. Eight studies (47%) reported both clinical outcomes and process measures, six (35%) reported only process measures, and two (12%) reported only clinical outcomes. Five studies (30%) reported no difference in clinical outcomes with AI, four (24%) reported improvement in clinical outcomes compared to controls, two (12%) reported positive effects on clinical outcomes with use of AI but had no formal comparison or controls, and one (6%) reported poor clinical outcomes with AI. Twelve studies (71%) reported improvement in process measures, while two (12%) reported no improvement. Five (30%) studies reported on at least 1 human performance measure.

Conclusions: While there are many published pediatric AI models, the number of AI implementations is minimal with no standardized reporting of outcomes, care processes, or human performance measures. More comprehensive evaluations will help elucidate mechanisms of impact.