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- W4288712110 endingPage "341" @default.
- W4288712110 startingPage "334" @default.
- W4288712110 abstract "Machine learning models may be integrated into clinical decision support (CDS) systems to identify children at risk of specific diagnoses or clinical deterioration to provide evidence-based recommendations. This use of artificial intelligence models in clinical decision support (AI-CDS) may have several advantages over traditional “rule-based” CDS models in pediatric care through increased model accuracy, with fewer false alerts and missed patients. AI-CDS tools must be appropriately developed, provide insight into the rationale behind decisions, be seamlessly integrated into care pathways, be intuitive to use, answer clinically relevant questions, respect the content expertise of the healthcare provider, and be scientifically sound. While numerous machine learning models have been reported in pediatric care, their integration into AI-CDS remains incompletely realized to date. Important challenges in the application of AI models in pediatric care include the relatively lower rates of clinically significant outcomes compared to adults, and the lack of sufficiently large datasets available necessary for the development of machine learning models. In this review article, we summarize key concepts related to AI-CDS, its current application to pediatric care, and its potential benefits and risks." @default.
- W4288712110 created "2022-07-30" @default.
- W4288712110 creator A5012369690 @default.
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- W4288712110 date "2022-07-29" @default.
- W4288712110 modified "2023-10-14" @default.
- W4288712110 title "Artificial intelligence-based clinical decision support in pediatrics" @default.
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- W4288712110 doi "https://doi.org/10.1038/s41390-022-02226-1" @default.