Pediatrics

Machine Learning Prediction of Unbound Ceftriaxone Concentrations in Children: Capturing Developmental Changes in Protein Binding for Precision Dosing.

The pharmacological activity of antimicrobial agents depends on unbound concentrations, but accurately estimating these free fractions remains challenging in pediatric patients due to developmental protein binding changes. This study aimed to develop a machine learning (ML) model to predict unbound ceftriaxone concentrations using routinely available clinical variables while capturing nonlinear de...

A Novel, Interpretable Machine Learning Model Predicts Furosemide Dosing After Congenital Cardiac Surgery.

Fluid overload is common after neonatal congenital cardiac surgery (CCS) and is frequently managed with continuous furosemide infusions requiring iterative dose titration. An interpretable prediction model could support more consistent early postoperative dosing decisions. We hypothesized that a novel, interpretable machine learning approach could accurately predict furosemide dosing decisions in ...

Can a Large Language Model Grounded in Text-Based Agency-Specific Prehospital Protocols Provide Accurate Care Recommendations?

OBJECTIVES: Large language models (LLMs) using a retrieval-augmented generation (RAG) approach have the ability to respond to user queries with answer...

Application of hiPSCs for tissue modeling and therapy: are we on track?

Stem cells are key for development of disease modeling and therapies. While promising, however, current application of cutting-edge hiPSC technologies...

A comparison of two- and three-dose MVA-BN mpox vaccination series on anti-monkeypox virus immunity.

The 2022 global outbreak of clade IIb mpox represented a turning point in public health's handling of poxviruses. The primary vaccine available for th...

Evaluation of large language models and retrieval-augmented generation for clinical reasoning in pediatric myopia: a 50-case real-world study.

To evaluate the clinical reasoning ability of large language models (LLMs) and retrieval-augmented generation (RAG) systems in pediatric myopia manage...

Machine learning and SHAP-based risk assessment of PICC-related bloodstream infections in premature infants at the time of clinical suspicion.

BACKGROUND: Peripherally inserted central catheter-related bloodstream infections (PICC-CRBSI) pose a serious threat to preterm infants. This study ai...

Pediatric lung ground glass nodules: a real-world, large-scale CT cohort analysis.

BACKGROUND: Increasing detection of pediatric ground-glass nodules (GGNs) presents a clinical dilemma lacking robust evidence and guidelines. We aimed...

Leveraging large language models on free-text symptoms from participatory surveillance enhances pertussis forecasting in the United States.

BACKGROUND: Declines in childhood vaccination in the U.S. have contributed to a resurgence of vaccine-preventable diseases, including a notable increa...

Smartphone-based lightweight AI system for real-time multiple anterior segment disease screening: development and real-world validation.

BACKGROUND: Anterior segment diseases are a major global cause of preventable blindness, especially in regions with limited access to specialized opht...

Advanced machine learning model for Cobb angle progression in adolescent idiopathic scoliosis with surface topography: a multicenter, prospective, observational study.

INTRODUCTION: Excessive radiographic exposure in the follow-up of adolescent idiopathic scoliosis (AIS) remains a clinical concern. Surface topography...

Towards reliable use of artificial intelligence to classify otitis media using otoscopic images: Addressing bias and improving data quality.

Ear disease contributes significantly to global hearing loss, with recurrent otitis media being a primary preventable cause in children, impacting dev...

Intelligent technologies in operating room nursing: A bibliometric analysis of research.

BACKGROUND: The integration of intelligent technologies in operating room nursing represents a rapidly evolving field. Intelligent operating room nurs...

Are chatbots reliable sources of information regarding fluoride in pediatric dentistry?

AIM: To evaluate the accuracy and consistency of responses generated by artificial intelligence (AI) chatbots in pediatric dentistry, specifically con...

AI chatbots in strabismus care: A multidomain expert evaluation of caregiver-facing information.

AimTo evaluate and compare the performance of five artificial intelligence (AI) chatbots-ChatGPT (OpenAI 4), Google Gemini, Grok (xAI), DeepSeek, and ...

The Pathophysiology of Spontaneous Preterm Birth: Emerging Mechanisms Reviewed by the Preterm Birth International Collaborative.

Around 10% of global births are preterm (before 37 weeks of gestation), posing a significant challenge to maternal and neonatal health. Preterm infant...

Design and Evaluation of a Faculty Development Workshop Series on Integrating Generative Artificial Intelligence in Medical Education: Mixed Methods Pilot Study.

BACKGROUND: Generative artificial intelligence (GenAI) tools are being increasingly applied to teaching and learning in medical education creating bot...

Factors Associated with Respite Care Use Among Veteran Caregivers: A Machine Learning Analysis.

BACKGROUND AND OBJECTIVES: Respite care provides temporary relief to family caregivers yet remains underused, and the factors shaping its utilization ...

[Artificial intelligence algorithm for bone age estimation].

BACKGROUND: Bone age (BA) estimation with automated methods can eliminate interindividual variation. OBJECTIVE: To evaluate BA by creating an artifici...

Machine learning-based information flow analysis of ECG signals for long QT syndrome.

Cardiovascular diseases remain the leading cause of death worldwide, highlighting the need for non-invasive and cost-effective risk assessment tools. ...