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Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review
Claudette O Adegboro1, Avishek Choudhury2, Onur Asan2
1Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin.
Insights
Artificial intelligence (AI) shows promise in pediatric intensive care, but few studies demonstrate direct health outcome improvements. More research is needed to validate AI
Area of Science:
- Pediatric Critical Care Medicine
- Medical Informatics
- Artificial Intelligence Applications
Background:
- Artificial intelligence (AI) is increasingly utilized in pediatrics.
- AI has the potential to enhance care for critically ill children in intensive care units (ICUs).
Purpose of the Study:
- To systematically review the use of AI in improving health outcomes for neonates and children in intensive care.
- To identify and categorize the impact of AI interventions in pediatric critical care settings.
Main Methods:
- A comprehensive literature search was conducted across PubMed, IEEE Xplore, Cochrane, and Web of Science databases.
- Peer-reviewed studies published between June 2010 and May 2020, focusing on AI, pediatrics, and intensive care, were included.
- Articles were assessed for direct or indirect impact on health outcomes, with data extraction performed by two independent researchers.
Main Results:
- Out of 287 publications, 32 met the inclusion criteria.
- Only 22% of studies (n=7) reported a direct, positive impact of AI on health outcomes, with most in prototype stages.
- The majority of AI models (78%) outperformed standard clinical methods, suggesting indirect influence on patient outcomes, though with significant heterogeneity in assessment metrics.
Conclusions:
- Current evidence for AI directly improving pediatric critical care outcomes is limited.
- Further prospective, experimental studies are essential.
- Standardized metrics, validated outcome measures, and established implementation frameworks are needed to accurately assess AI's impact.
Context:
Artificial intelligence (AI) technologies are increasingly used in pediatrics and have the potential to help inpatient physicians provide high-quality care for critically ill children.
Objective:
We aimed to describe the use of AI to improve any health outcome(s) in neonatal and pediatric intensive care.
Data Source:
PubMed, IEEE Xplore, Cochrane, and Web of Science databases.
Study Selection:
We used peer-reviewed studies published between June 1, 2010, and May 31, 2020, in which researchers described (1) AI, (2) pediatrics, and (3) intensive care. Studies were included if researchers assessed AI use to improve at least 1 health outcome (eg, mortality).
Data Extraction:
Data extraction was conducted independently by 2 researchers. Articles were categorized by direct or indirect impact of AI, defined by the European Institute of Innovation and Technology Health joint report.
Results:
Of the 287 publications screened, 32 met inclusion criteria. Approximately 22% (n = 7) of studies revealed a direct impact and improvement in health outcomes after AI implementation. Majority were in prototype testing, and few were deployed into an ICU setting. Among the remaining 78% (n = 25) AI models outperformed standard clinical modalities and may have indirectly influenced patient outcomes. Quantitative assessment of health outcomes using statistical measures, such as area under the receiver operating curve (56%; n = 18) and specificity (38%; n = 12), revealed marked heterogeneity in metrics and standardization.
Conclusions:
Few studies have revealed that AI has directly improved health outcomes for pediatric critical care patients. Further prospective, experimental studies are needed to assess AI's impact by using established implementation frameworks, standardized metrics, and validated outcome measures.
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