Appl Clin Inform 2025; 16(03): 477-487
DOI: 10.1055/a-2521-1508
Review Article

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

Swaminathan Kandaswamy
1   Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
,
Lindsey A. Knake
2   Division of Neonatology, Department of Pediatrics, University of Iowa, Iowa City, Iowa, United States
,
Adam C. Dziorny
3   Department of Pediatrics, University of Rochester, Rochester, New York, United States
,
Sean M. Hernandez
4   Primary Care, Miami Veteran's Affairs, Miami, Florida, United States
,
Allison B. McCoy
5   Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Lauren M. Hess
6   Pediatric Hospital Medicine, Texas Children's Hospital, Houston, Texas, United States
7   Division of Pediatric Hospital Medicine, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, United States
,
Evan Orenstein
1   Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
8   Division of Hospital Medicine, Children's Healthcare of Atlanta, Atlanta, Georgia, United States
,
Mia S. White
9   Woodruff Health Sciences Center Library, Emory University, Atlanta, Georgia, United States
,
Eric S. Kirkendall
10   Department of Pediatrics, Wake Forest University School of Medicine, Center for Healthcare Innovation, Winston-Salem, North Carolina, United States
,
Matthew J. Molloy
11   Department of Pediatrics, Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
,
Philip A. Hagedorn
11   Department of Pediatrics, Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
,
Naveen Muthu
1   Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
8   Division of Hospital Medicine, Children's Healthcare of Atlanta, Atlanta, Georgia, United States
,
Avinash Murugan
12   Department of Internal Medicine, Yale New Haven Hospital, New Haven, Connecticut, United States
,
Jonathan M. Beus
1   Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
8   Division of Hospital Medicine, Children's Healthcare of Atlanta, Atlanta, Georgia, United States
,
Mark Mai
1   Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
8   Division of Hospital Medicine, Children's Healthcare of Atlanta, Atlanta, Georgia, United States
,
Brooke Luo
13   Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
14   Department of Pediatrics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States
,
Juan D. Chaparro
15   Division of Clinical Informatics, Department of Pediatrics, Nationwide Children's Hospital/Ohio State University College of Medicine, Columbus, Ohio, United States
› Institutsangaben

Funding None.
Preview

Abstract

Objective

To review pediatric artificial intelligence (AI) implementation studies from 2010 to 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 and 10/2021; must have agency (AI must provide guidance that affects clinical care, not merely running in the 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 with 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.

Conclusion

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.

Protection of Human and Animal Subjects

None.


Supplementary Material



Publikationsverlauf

Eingereicht: 09. September 2024

Angenommen: 20. Januar 2025

Accepted Manuscript online:
21. Januar 2025

Artikel online veröffentlicht:
28. Mai 2025

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