Pediatrics

Nurses' artificial intelligence anxiety levels, their attitudes towards artificial intelligence, and their skills in acting humanistically in nursing practices.

AIM: This study aims to investigate the levels of artificial intelligence-related anxiety among nurses, their attitudes towards the use of AI in clinical settings, their ability to maintain humanistic approaches in nursing care, and the interrelationships among these variables. DESIGN: This study was designed as a descriptive and cross-sectional pilot study. METHOD: The sample of the descriptive s...

Optimizing pediatric chest CT: superior image quality and lower radiation dose with deep learning reconstruction.

OBJECTIVES: To compare image quality and radiation dose between deep learning reconstruction (DLIR) and hybrid iterative reconstruction (HIR) algorithms in unenhanced pediatric chest CT. MATERIALS AND METHODS: This hybrid prospective-retrospective study included 142 pediatric patients (<16 years) who underwent single-phase unenhanced chest CT between 2021 and 2024. The DLIR cohort was prospectivel...

Feasibility of a Large Language Model Chatbot to Support Parental Understanding in the PICU.

OBJECTIVES: To evaluate the feasibility of a large language model (LLM)-based chatbot for answering parental questions in the PICU and inform design o...

A practical framework for operationalising responsible and equitable artificial intelligence in health care: tackling bias, inequity, and implementation challenges.

Artificial intelligence (AI) has the potential to transform health care; however, successful integration of AI into health care requires overcoming ob...

Meta-analysis, WGCNA, and machine learning converge on a four-gene biomarker panel for heat stress tolerance in Solanum lycopersicum.

Heat stress severely compromises growth, reproduction, and yield in Solanum lycopersicum, necessitating the identification of reliable molecular bioma...

International testing and refinement of AI algorithms predicting acute leukemia subtypes from routine laboratory data.

Despite advances for patients with acute leukemia health disparities limit access to diagnosis and treatment. Artificial Intelligence (AI) approaches ...

Variable Treatment Effects of Cranial Surgery in Pediatric Severe Traumatic Brain Injury.

BACKGROUND AND OBJECTIVES: Severe traumatic brain injury (TBI) in children is associated with poor outcomes, but evidence surrounding the role of oper...

Scalable and Robust Artificial Intelligence for Spine Alignment Assessment: Multicenter Study Enabled by Real-Time Data Transformation.

BACKGROUND: Artificial intelligence (AI) has shown promise for automating spinal alignment assessment in adolescent idiopathic scoliosis (AIS). Howeve...

HealthData@MAD-R&I: Protocol for Design and Development of a Regional Health Data Infrastructure to Enable Secondary Use of Health Data in Research and Innovation.

BACKGROUND: The exponential growth of electronic health records (EHRs), together with the recent entry into force of the European Health Data Space (E...

Explainable Artificial Intelligence for Predicting Length of Stay and Treatment Response in Pediatric Asthma and Allergic Rhinitis: An Irregular Fuzzy Cellular Automata Approach Across Pre- and During-COVID-19 Periods.

OBJECTIVE: The COVID-19 pandemic significantly altered treatment strategies and healthcare utilization patterns in pediatric respiratory diseases. Giv...

TF-VSF: A Novel Training-Free Visual-Semantic Fusion Rare Medical Morning Glory Syndrome Diseases Severity Assessment Method.

Medical morning glory syndrome (MGS) is a rare congenital disease. Approximately 50% of MGS patients present with retinal detachment. Widespread scree...

Improved BG-PVS Quantification in Infant Brain MRI Using Anatomy-Informed Pseudo-Labels for Joint BG and PVS Segmentation.

BACKGROUND: Reliable quantification of perivascular spaces (PVS) in the basal ganglia (BG) is of growing interest for understanding the glymphatic sys...

DeepSeek-R1 performance in fusion planning of adolescent idiopathic scoliosis: A preliminary study.

BACKGROUND: Surgical planning for adolescent idiopathic scoliosis (AIS) is complex. Large language models (LLMs) like DeepSeek Reasoning Model R1 (Dee...

Artificial Intelligence in Pediatric Oncology in Africa: A Survey of Awareness, Use, and Readiness Amongst Healthcare Workers.

INTRODUCTION: Artificial intelligence (AI) has the potential to enhance oncology diagnostics, treatment planning, and patient monitoring. In pediatric...

Public Engagement Strategies in Digital Health Ethics: Protocol for a Scoping Review.

BACKGROUND: The ethical, legal, and social issues accompanying the latest advancements in digital health technologies highlight the need to involve th...

Integrating biological and machine learning models for rainbow trout growth: Balancing accuracy and interpretability.

Invasive species management demands predictive models that balance accuracy with ecological interpretability, yet traditional approaches often fail to...

Machine learning prediction of positive urine cultures in an academic pediatric emergency department (ED).

BACKGROUND: Urinary tract infections (UTIs) are among the most common pediatric infections, but urine culture, the diagnostic gold standard, requires ...

A Pilot Study to Evaluate Artificial Intelligence-Driven Early Retrieval of Medical Histories in the Emergency Department.

STUDY OBJECTIVE: To assess the feasibility and acceptability of using ChatGPT to obtain histories of present illnesses directly from patients or careg...

Diffusion-Augmented Spatio-Temporal Graph Convolution for Clinical Gait and Motor Function Assessment.

Accurate assessment of gross motor function in children with cerebral palsy (CP) is essential for clinical decision-making, yet current practice is li...

Approval of AI-Based Medical Devices in China From 2020 to 2025: Retrospective Analysis.

BACKGROUND: Artificial intelligence-based medical devices (AIMDs) have emerged as transformative technologies in modern health care. However, comprehe...