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.
Objective: The objectives of this study are to synthesize findings from recent research of retrieval-augmented generation (RAG) and large language models (LLMs) in biomedicine and provide clinical development guidelines to improve effectiveness.
Materials And Methods: We conducted a systematic literature review and a meta-analysis. The report was created in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 analysis.
The development of perfusable and multiscale vascular networks remains one of the largest challenges in tissue engineering. As such, there is a need for the creation of customizable and facile methods to produce robustly vascularized constructs. In this study, secondarily crosslinkable (clickable) poly(ethylene glycol)-norbornene (PEGNB) microbeads were produced and evaluated for their ability to sequentially support suspension bioprinting and microvascular self-assembly towards the aim of engineering hierarchical vasculature.
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