Latest AI and machine learning research in pediatrics for healthcare professionals.
Image-to-video (I2V) generation has the potential for societal harm because it enables the unauthorized animation of static images to create realistic deepfakes. While existing defenses effectively protect against static image manipulation, extending these to I2V generation remains underexplored and non-trivial. In this paper, we systematically analyze why modern I2V models are highly robust again...
Brain development follows a precisely regulated biological timetable, with defined periods of vulnerability increasingly recognized in congenital disorders affecting early brain development. This biological timing can be captured by the emerging concept of brain age, a measure of brain maturation, enabling the detection of deviation from normative developmental trajectories. Clinical conditions af...
Background: Extrauterine growth restriction (EUGR) is a common and clinically significant complication among preterm infants, contributing to adverse ...
During the COVID-19 pandemic, reported incidence data played a central role in public health surveillance and in tracking epidemic dynamics, although ...
Neonates requiring intensive care are at increased risk for long-term neuropsychiatric disorders. However, clinical adoption of risk prediction models...
Healthcare system performance evaluation is constrained by episodic performance indicators and process mining techniques that fail to accommodate the ...
Adolescent major depressive disorder (AMDD) is a prevalent and heterogeneous psychiatric condition that emerges during a critical period of brain deve...
Deep learning (DL) models have achieved strong performance in an intelligence healthcare setting, yet most existing approaches operate as black boxes ...
Background: The advancement of single cell technologies has driven significant progress in constructing a multiscale, pan-organ Human Reference Atlas ...
Reliable machine-learning models in biomedical settings depend on accurate labels, yet annotating biomedical time-series data remains challenging. Alg...
Background: Artificial intelligence (AI) has rapidly garnered interest in healthcare, with research showing promise to improve quality, efficiency, an...
Pediatric bipolar disorder is challenging to diagnose accurately due to symptom heterogeneity. More standardized and data-driven approaches are needed...
Background Narcolepsy is a rare, lifelong neurological disorder that often begins in childhood or adolescence. Diagnosis is frequently delayed because...
Virtually every sector of society is experiencing a dramatic growth in the volume of unstructured textual data that is generated and published, from n...
Single-cell omics has extended the biological interrogation of cell state from examining the expression of individual genes to unbiased profiling of t...
Importance: People living with rare diseases (PLWRD) often face significant challenges in receiving timely and accurate diagnoses, leading to what is ...
Early detection of atypical cognitive-motor development is critical for timely intervention, yet traditional assessments rely heavily on subjective, s...
Introduction: Timely, protocol-adherent clinical decisions are crucial for reducing neonatal mortality in low-resource settings. Translating extensive...
Systems powered by large language models are widely used for health information and advice, yet robust evidence for their safety and effectiveness in ...
Objective: To develop and validate a multivariable prediction model and clinically actionable risk score for vaginal birth after cesarean (VBAC) succe...