Pediatrics

Multidisciplinary research priorities for artificial intelligence in mental health: a call to action.

The use of artificial intelligence (AI) is anticipated to transform mental health care. However, the rapid research growth in this field has outpaced coordinated frameworks, leaving research efforts fragmented, standards inconsistent, and safeguards for safety and ethics largely absent. This Position Paper outlines a coordinated roadmap to guide the responsible evaluation and implementation of AI ...

Transformer-based classification with enhanced causal explainability from otoscopic images.

Otitis media is a major cause of hearing loss, particularly in children. However, nonspecific symptoms and subjective evaluations make its diagnosis challenging. To address this, we developed transformer-based models to classify tympanic membrane conditions from otoscopic images. This approach aims to enhance diagnostic transparency and reliability in clinical settings. We trained vision transform...

Integrated framework for pediatric height assessment: X-ray-based height extreme cases classification and machine learning for multivariate height prediction.

Pediatric height assessment is crucial for evaluating growth and enabling timely intervention. However, traditional methods primarily assess current b...

Plant Biofactories for Vaccination: Progress in Edible Vaccine Technologies as a Sustainable Approach to Global Immunization.

Globally, the pathogen-driven enteric diseases remain a leading cause of morbidity and mortality, disproportionately affecting populations in low-inco...

Emerging Novel SARS-CoV-2 Subvariants and the Advantages of AI-ML in Deciphering the Mutation Trends, Genomic Surveillance and Vaccine Development.

BACKGROUND AND AIM: The world witnessed COVID-19 in 2025 yet again, a resurgence driven by LF.7 and NB.1.8.1 subvariants of JN.1. With Singapore, Hong...

A near-standard soil spectral library to improve stability and transferability of hyperspectral remote sensing models for soil heavy metal prediction.

Limited sample sizes and regional differences in pollution characteristics and soil properties often obscure the characteristic bands of soil heavy me...

A method for early ultrasound classification of developmental dysplasia of the hip in infants: combining YOLOv8-based keypoint detection with radiomics.

This study introduces an innovative method for early ultrasound classification of developmental dysplasia of the hip (DDH) in infants, integrating key...

Predicting Psychiatric Readmission of Children and Adolescents Using Machine Learning.

INTRODUCTION: Recurrent psychiatric hospitalizations in youth are costly and burdensome to patients and families. While postdischarge planning rarely ...

Service Robots as Work Support for Health Personnel in Long-Term Care: Protocol for a Scoping Review.

BACKGROUND: Demographic shifts are increasing the global demand for long-term care services, coinciding with a worldwide shortage of health care perso...

The rise of medical autonomous care, a paradigmatic turning point for military and civilian delivery of health care.

RATIONALE: Resource-limited or austere environments represent a direct threat to the likelihood of survival of patients in need of emergent care. Medi...

Machine learning approaches in automated infant General Movements Assessment: A scoping review of current practice and challenges.

Evaluating the role of youth-onset diabetes mellitus in the context of food insecurity and preventive care: a repeated cross-sectional study using propensity score weighted machine learning analysis.

BACKGROUND: Youth-onset diabetes mellitus (DM) is an increasing public health concern, especially among adolescents facing socioeconomic challenges. F...

Development of a target product profile for artificial intelligence in diabetic eye screening in England: a modified Delphi consensus study.

Artificial intelligence (AI) health-care technologies offer a means of addressing the growing gap between health-care capacity and demand. However, fe...

Development and validation of a deep learning model for radiographic classification of pediatric femoral neck fractures.

BACKGROUND: The Delbet-Colonna (DC) classification guides treatment of pediatric femoral neck fractures (PFNFs) but relies on clinical experience. No ...

Predicting COVID-19 severity using machine learning: the role of clinical parameters, blood groups, and vaccination status across age groups.

OBJECTIVES: COVID-19 has had strong impacts on both global health systems and economies. Therefore, understanding disease severity and progression is ...

Explainable machine learning for the early differentiation of pediatric bronchopneumonia using routine laboratory parameters.

OBJECTIVE: Early differentiation of pediatric acute respiratory infections in outpatient and emergency settings is often hindered by nonspecific clini...

Artificial Intelligence in Pharmaceutical Care: Transforming Drug Discovery, Development, and Patient-care Delivery.

The integration of artificial intelligence (AI) into pharmaceutical care represents a paradigm shift in healthcare delivery, offering unprecedented op...

Can Machine Learning Transform the Diagnostic Approach to Congenital Disorders of Adrenal Steroidogenesis?

Bridging the global Parkinson's divide: Technology as a structural solution for equitable and brain health-integrated care.

Parkinson's disease (PD) is the fastest-growing neurodegenerative disorder worldwide, with projections exceeding 25 million people by 2050. Its burden...

Multi-method statistical signal aggregation with machine learning for severity classification of neonatal adverse drug reactions.

Neonates represent one of the most pharmacologically vulnerable patient populations, yet they remain systematically underrepresented in clinical drug ...