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To review pediatric artificial intelligence (AI) implementation studies from 2010 to 2021 and analyze reported performance measures.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 [...]

Cerebral aneurysms (CAs) affect 3 to 5% of the general population, with saccular aneurysms being the most common type. Despite advances in treatment, patient understanding of CAs and associated procedures remains limited, impacting informed consent and treatment outcomes.This pilot study aims to evaluate the effectiveness of mixed reality (MR) technology in enhancing patient education and understanding of cerebral angiograms and aneurysm treatment, thereby improving the patient-surgeon communication process.A nonrandomized single-center [...]

screening is a labor-intensive component of systematic review involving repetitive application of inclusion and exclusion criteria on a large volume of studies. We aimed to validate large language models (LLMs) used to automate abstract screening.

Extended reality (XR) applications are gaining support as a method of reducing anxieties about medical treatments and conditions; however, their impacts on health service inequalities remain underresearched. We therefore undertook a synthesis of evidence relating to the equity implications of these types of interventions.

To validate a phenotyping algorithm for gradations of diverticular disease severity and investigate relationships between unmet social needs and disease severity.

While performance drift of clinical prediction models is well-documented, the potential for algorithmic biases to emerge post-deployment has had limited characterization. A better understanding of how temporal model performance may shift across subpopulations is required to incorporate fairness drift into model maintenance strategies.

This study aimed to develop a novel multi-stage self-supervised learning model tailored for the accurate classification of optical coherence tomography (OCT) images in ophthalmology reducing reliance on costly labeled datasets while maintaining high diagnostic accuracy.

Pediatric cancer patients with fever and neutropenia are at risk for bacterial sepsis, traditionally requiring extended hospital stays on antibiotics until neutrophil counts recover. According to a newly validated scoring system, a subset of these patients is at lower risk and eligible for early discharge and reduced intravenous (IV) antibiotic exposure.Reduce length-of-stay (LOS) for febrile neutropenic patients using clinical decision support (CDS) to identify low-risk patients.A CDS system was developed [...]

Experiences sharing complex workflow-integrated clinical decision support (CDS) across health systems are sparse and not well reported. This case study presents the sharing of a hybrid electronic health record (EHR)-native and SMART-compatible CDS tool for automating provision of smoking cessation treatment for caregivers during pediatric visits.We conducted a comprehensive needs assessment using sociotechnical frameworks to identify workflow gaps and technical requirements. A multidisciplinary team of clinical informaticians, software developers, and [...]