Celiac disease (CD) is a chronic autoimmune condition traditionally recognized for its gastrointestinal symptoms. However, growing evidence indicates that CD can also affect social and emotional health, particularly among children. This narrative review explores how the autoimmunity of CD may contribute to social–emotional dysregulation through mechanisms such as neuroinflammation, nutrient deficiencies, and disruption of the gut–brain axis. It summarizes the current literature on anxiety, attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and autism spectrum disorder (ASD), highlighting how immune dysregulation may influence children’s social–emotional wellbeing. Delayed diagnosis, poor dietary adherence, and ongoing inflammation were recognized among children with social–emotional dysregulation. While digestive problems are commonly recognized and treated, social–emotional dysregulation among children with CD is frequently overlooked. However, a gluten-free diet without a confirmed diagnosis of CD is not sufficient to improve social–emotional outcomes. Children presenting with social–emotional dysregulation and clinical features suggestive of CD should be screened using standard serology and, when indicated, biopsy. Starting a gluten-free diet (GFD) without a confirmed diagnosis is not recommended. While mechanistic pathways are described, most evidence remains observational and clinically descriptive, underscoring the need for longitudinal and experimental studies to understand the intersectionality of CD with social–emotional dysregulation.

Background: Radiology underpins diagnosis and treatment across pediatrics, yet most artificial intelligence (AI) tools are developed for adults and validated on adult datasets only. Of more than 200 AI systems cleared by the United States (U.S.) Food and Drug Administration (FDA), only about 3% include pediatric validation. Because children differ from adults in anatomy, physiology, pathology, epidemiology, and imaging protocols, adult-trained models often perform sub-optimally in pediatric settings. Methods: A narrative review of peer-reviewed literature from 2000 to 2025 was conducted using PubMed, MEDLINE, Google Scholar, and Scopus. Studies involving AI applications in pediatric X-ray, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), echocardiography, and point-of-care ultrasound with quantitative performance metrics were included. Findings were synthesized by imaging modality, clinical task, and differences between high-income countries (HICs) and low- and middle-income countries (LMICs). Results: AI demonstrated strong performance across multiple pediatric imaging tasks. In X-ray interpretation, AI detected fractures with area under the curve (AUC) values up to 0.96 (sensitivity, 90.8%; specificity, 88.7%). Pneumonia classification achieved 76.5% accuracy, and foreign body aspiration detection showed 95.3% specificity in HICs. In ultrasound, AI improved junior sonographers’ detection of intussusception (AUC 0.857 to 0.966) and reduced scan time by more than 50%. AI-assisted bone age estimation achieved a mean error of 0.39 years. In echocardiography, AI-derived ejection fraction showed excellent agreement with experts’ interclass correlation coefficient (ICC 0.983), and AI support improved atrioventricular septal defect detection (84.4% to 86.5%). In MRI, the use of AI enhanced lesion detection and supported quantitative analysis. Deep-learning models trained on routine T1- and T2-weighted sequences predicted liver stiffness across multi-site datasets, while advanced neuroimaging pipelines improved the identification of subtle epileptogenic lesions that are often missed on conventional pediatric MRI. However, adult-trained models showed limited generalizability to children. Still, excluding children under the age of two years improved the reading accuracy of pediatric chest X-rays (CXRs) by adult-trained models from 88% to 97%. AI faces challenges beyond the development of age-specific models. Substantial heterogeneity, limited pediatric-specific datasets, and unresolved medicolegal responsibility further restrict adoption worldwide. Challenges are amplified in LMICs, where unstable electricity, limited radiology resources, weak digital infrastructure, and scarce pediatric providers limit implementation. Additionally, many large language models underperform and lack inclusive algorithms suitable for pediatric radiology in many LMICs. Conclusions: AI can enhance diagnostic accuracy, efficiency, and access to pediatric imaging, particularly in resource-limited settings, through task-shifting and decision support. However, it cannot replace pediatric radiologists as of today. Safe adoption requires pediatric-specific model development, standardized validation metrics, diverse datasets that include LMIC populations, stronger digital infrastructure, robust radiologist training in AI capabilities, and the establishment of clear guidelines and medicolegal policies.