Pediatrics

AI-enhanced system dynamics simulation for sustainable urban development under water constraints: A case study of Langfang, China.

Water-scarce cities undergoing rapid urbanization must sustain economic growth while complying with stringent water-resource and environmental constraints. However, long-term policy simulations are often affected by uncertainty in exogenous drivers, particularly population dynamics. This study develops an AI-enhanced system dynamics (SD) framework that couples a BP neural-network ensemble forecast...

Predicting length of stay in the pediatric intensive care unit at a tertiary center in Saudi Arabia using machine learning.

BACKGROUND: Prolonged stay in pediatric intensive care units (PICUs) is associated with increased mortality risk, elevated healthcare costs, and diminished critical care capacity. Accurate early prediction of length of stay (LOS) may facilitate resource allocation, discharge planning, and family counseling. Traditional regression-based models have demonstrated limited performance because of the co...

GHOSTS: Validated generation of synthetic hospital time series.

Machine learning (ML) holds great promise to support, improve, and automatize clinical decision-making in hospitals. Model training on abundantly avai...

Artificial Intelligence Applications in General Surgery in the United States of America: A Bibliometric Analysis.

BACKGROUND: Artificial intelligence (AI) is rapidly transforming surgical practice, with applications spanning preoperative planning, intraoperative g...

Development of a machine learning-based prognostic prediction model and a web-based tool for pediatric hepatoblastoma: a Surveillance, Epidemiology, and End Results (SEER) database study.

BACKGROUND: Prognostic evaluation of pediatric hepatoblastoma (HB) remains challenging due to the low accuracy of traditional risk stratification mode...

AI-based diagnostic evaluation of GPT-4o for crown-fracture detection on maxillary periapical radiographs: effects of prompt detail and customization.

BACKGROUND/AIM: Artificial intelligence (AI) and large language models (LLMs) are rapidly entering dental imaging workflows. We conducted a diagnostic...

Safety and diagnostic accuracy of large-language model application of PECARN head injury algorithm.

BACKGROUND: Clinical decision algorithms used by clinicians guide evidence-based decisions and actions. Automated tools can help with the adoption and...

Letter to the Editor Regarding: "OrthoKids and OrthoInfo Response to Training a Bilingual Artificial Intelligence Model on Pediatric Orthopaedic Resources to Enhance the Readability of Patient Education Materials".

Brain-age defines a structural signature of outcomes after vagus nerve stimulation in children.

INTRODUCTION: Outcomes following vagus nerve stimulation (VNS) are difficult to predict prior to surgery in pediatric drug-resistant epilepsy (DRE). W...

Establishing a Sustainable Artificial Intelligence Infrastructure for Health Care Systems Part II: Consideration of Key Regulatory and Bioethical Barriers to Implementation.

Establishing an artificial intelligence (AI) infrastructure is contingent upon processes being conducted within structured regulatory frameworks that ...

Digital twins and multimodal artificial intelligence in spine care: a scoping review of concepts, evidence, and translational barriers.

PURPOSE: This scoping review examines current evidence supporting multimodal artificial intelligence, continuous monitoring, and digital twin concepts...

Explainable AI for Well-Being Prediction From Lifestyle Data: 2-Study Design.

BACKGROUND: Well-being is a cornerstone of public health and social progress; yet, its determinants are multifaceted and dynamic. As behavioral data b...

Predicting endoscopic third ventriculostomy with choroid plexus cauterization success with machine learning: the importance of ventricle size and the irrelevance of etiology.

OBJECTIVE: Endoscopic third ventriculostomy with choroid plexus cauterization (ETV/CPC) has decreased rates of shunt dependence in infants with hydroc...

Benchmarking large language models on persian surgical subspecialty board examinations: a comparative study of ChatGPT-4o, ChatGPT-5, and Gemini 2.5 Flash.

This study evaluated the performance of three large language models, including ChatGPT-4o, ChatGPT-5, and Gemini 2.5 Flash, on 532 Persian multiple-ch...

dAMN: a genome-scale neural-mechanistic hybrid model to predict bacterial growth dynamics.

SUMMARY: This study presents dAMN, a genome-scale neural-mechanistic hybrid model that combines neural networks with dynamic flux balance analysis to ...

Diagnostic accuracy of artificial intelligence versus 263 pediatric clinicians for childhood exanthems.

Pediatric exanthematous diseases pose diagnostic challenges because clinical presentations overlap. To determine whether current artificial intelligen...

GenAI-Supported Virtual Patients in Health Care Education: Systematic Review.

BACKGROUND: Generative artificial intelligence (GenAI) is enhancing virtual patient simulations in health care education by enabling dynamic, adaptive...

Predicting first onset of suicide attempt among children with suicidal ideation or non-suicidal self-injury using machine learning: a prospective population-based cohort study.

While children with suicidal ideation or non-suicidal self-injury (NSSI) are at high risk of suicide, most do not attempt suicide. This study aims to ...

Digital emergency routing: analysis of feasibility, utilization, and equity implications.

BACKGROUND: Digital emergency care applications offer potential to reduce delays, enhance triage, and improve care coordination, yet evidence remains ...

Machine learning reveals proteome-encoded growth determinants underlying metabolic versatility of Rhodopseudomonas palustris on lignin-derived aromatics.

Lignin depolymerization generates mixtures of aromatic compounds that are promising carbon sources for microbial bioconversion, yet the constraints go...