Pediatrics

Latest AI and machine learning research in pediatrics for healthcare professionals.

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Closing the Pediatric Divide: A Performance Analysis of the GPT-5 Family in Medical Diagnostics

Large Language Models (LLMs) have demonstrated significant potential in clinical medicine, but a persistent performance gap exists in the pediatric domain due to its unique complexities. This study provides the first comparative evaluation of the new GPT-5 family (Nano, Mini, and full) to assess the impact of model scale on diagnostic accuracy and this specific adult-pediatric disparity. A benchma...

External Validation of Predictive Models for Diagnosis, Management and Severity of Pediatric Appendicitis

Appendicitis is a common condition among children and adolescents. Machine learning models can offer much-needed tools for improved diagnosis, severity assessment and management guidance for pediatric appendicitis. However, to be adopted in practice, such systems must be reliable, safe and robust across various medical contexts, e.g., hospitals with distinct clinical practices and patient populati...

Automated and interoperable methods for generalizable development of clinical machine-learning models for predicting neuromorbidity in critically ill children

To streamline the development of clinical machine learning (ML) models for predicting acute neurological morbidity in critically ill children by exten...

Antisense oligonucleotide depletion of CCDC146 is a broad-spectrum therapeutic strategy for ALS

Amyotrophic lateral sclerosis (ALS) is a heritable and incurable disease defined by the degeneration of motor neurons (MNs), yet the genetics of ALS r...

Predicting coronary artery abnormalities in Kawasaki disease: Model development and external validation

Kawasaki disease (KD) is an acute, pediatric vasculitis associated with coronary artery abnormality (CAA) development. Echocardiography at month 1 pos...

Reading Between the Signs: Predicting Future Suicidal Ideation from Adolescent Social Media Texts

Suicide is a leading cause of death among adolescents (aged 12–18), yet predicting it remains a significant challenge. Many cases go undetected becaus...

Comparative Analysis of Long COVID and Post-Vaccination Syndrome: A Cross-Sectional Study of Clinical Symptoms and Machine Learning-Based Differentiation

Long COVID is a well-documented post-viral syndrome, while post-vaccination syndrome (PVS) remains poorly characterized. Understanding their similarit...

Detecting Stigmatizing Language in Clinical Notes with Large Language Models for Addiction Care

Recent studies have found that stigmatizing terms can incline physicians to pursue punitive approaches to patient care. The intensive care unit (ICU) ...

Predicting Mental and Psychomotor Delay in Very Pre-term Infants using Large Language Models

Very preterm infants face a considerably higher risk of neurodevelopmental delays, making early diagnosis and timely intervention crucial for improvin...

An ensemble method associates prepregnancy BMI and maternal ethnicity with key cord blood metabolomic changes in a multi-ethnic cohort from Hawaii

Maternal obesity poses significant risks to fetal health, influencing metabolomic profiles in newborn cord blood. Despite the growing application of m...

Sex-Specific Diagnostic Subtypes in Adolescents Hospitalized for Substance Use Disorders Revealed by Transformer-Based Clustering

Substance use disorders (SUD) are a leading cause of psychiatric hospitalization among adolescents, yet the underlying diagnostic profiles and comorbi...

Using deep learning methods to shorten acquisition time in children’s renal cortical imaging

This study evaluates the capability of diffusion-based generative models to reconstruct diagnostic-quality renal cortical images from reduced-acquisit...

Beyond Accuracy in Small Open-Source Medical Large Language Models for Pediatric Endocrinology

Small open-source medical large language models (LLMs) offer promising opportunities for low-resource deployment and broader accessibility. However, t...

Responsible AI in Action: Planning through Implementation of a Mortality Model for Palliative Care

Interest in the use of prediction models to support referrals to palliative care is surging. Few high-performing models have been developed, implement...

Natural Language Processing to Build a Multicenter Computable Phenotype Library for Adults with Congenital Heart Disease

Our objective was to build classifiers for multiple phenotypes that categorize a cohort of adults with congenital heart disease (ACHD), that can be us...

Signal Mining and Analysis of Adverse Events of Isotretinoin: 20-year real-world pharmacovigilance analysis based on the FAERS database

To identify post-marketing adverse event (AE) signals associated with isotretinoin using real-world data from the U.S. Food and Drug Administration (F...

Longitudinal development of sex differences in the limbic system is associated with age, puberty and mental health

Sex differences in mental health become more evident across adolescence, with a two-fold increase of prevalence of mood disorders in females compared ...

Toward Non-Invasive Voice Restoration: A Deep Learning Approach Using Real-Time MRI

Despite recent advances in brain–computer interfaces (BCIs) for speech restoration, existing systems remain invasive, costly, and inaccessible to indi...

Epigenetic patient stratification reveals a sub-endotype of type 2 asthma with altered B-cell response

Despite biomarker-guided treatment strategies, clinical outcomes among patients with type 2 (T2)-high asthma remain heterogeneous, with some patients ...

Towards Participatory Precision Health: Systematic Review and Co-designed Guidelines For Adolescent Just-in-time Adaptive Interventions

Adolescence and young adulthood (10-25 years) constitute a sensitive developmental period marked by rapid biological, psychological, and social change...

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