Survivors of neonatal infections are at risk of neurodevelopmental impairment (NDI), a burden not previously systematically quantified and yet important for program priority setting. Systematic reviews and meta-analyses were undertaken and applied in a three-step compartmental model to estimate NDI cases after severe neonatal bacterial infection in South Asia, sub-Saharan Africa, and Latin America in neonates of >32 wk gestation (or >1,500 g). We estimated cases of sepsis, meningitis, pneumonia, or no severe bacterial infection from among estimated cases of possible severe bacterial infection ((pSBI) step 1). We applied respective case fatality risks ((CFRs) step 2) and the NDI risk among survivors (step 3). For neonatal tetanus, incidence estimates were based on the estimated deaths, CFRs, and risk of subsequent NDI. For 2010, we estimated 1.7 million (uncertainty range: 1.1-2.4 million) cases of neonatal sepsis, 200,000 (21,000-350,000) cases of meningitis, 510,000 cases (150,000-930,000) of pneumonia, and 79,000 cases (70,000-930,000) of tetanus in neonates >32 wk gestation (or >1,500 g). Among the survivors, we estimated moderate to severe NDI after neonatal meningitis in 23% (95% confidence interval: 19-26%) of survivors, 18,000 (2,700-35,000) cases, and after neonatal tetanus in 16% (6-27%), 4,700 cases (1,700-8,900). Data are lacking for impairment after neonatal sepsis and pneumonia, especially among those of >32 wk gestation. Improved recognition and treatment of pSBI will reduce neonatal mortality. Lack of follow-up data for survivors of severe bacterial infections, particularly sepsis, was striking. Given the high incidence of sepsis, even minor NDI would be of major public health importance. Prevention of neonatal infection, improved case management, and support for children with NDI are all important strategies, currently receiving limited policy attention.

Background/Objectives: Artificial intelligence (AI) and machine learning (ML) are transforming healthcare by enabling predictive, diagnostic, and therapeutic advancements. Pediatric healthcare presents unique challenges, including limited data availability, developmental variability, and ethical considerations. This narrative review explores the current trends, applications, challenges, and future directions of ML in pediatric healthcare. Methods: A systematic search of the PubMed database was conducted using the query: (“artificial intelligence” OR “machine learning”) AND (“pediatric” OR “paediatric”). Studies were reviewed to identify key themes, methodologies, applications, and challenges. Gaps in the research and ethical considerations were also analyzed to propose future research directions. Results: ML has demonstrated promise in diagnostic support, prognostic modeling, and therapeutic planning for pediatric patients. Applications include the early detection of conditions like sepsis, improved diagnostic imaging, and personalized treatment strategies for chronic conditions such as epilepsy and Crohn’s disease. However, challenges such as data limitations, ethical concerns, and lack of model generalizability remain significant barriers. Emerging techniques, including federated learning and explainable AI (XAI), offer potential solutions. Despite these advancements, research gaps persist in data diversity, model interpretability, and ethical frameworks. Conclusions: ML offers transformative potential in pediatric healthcare by addressing diagnostic, prognostic, and therapeutic challenges. While advancements highlight its promise, overcoming barriers such as data limitations, ethical concerns, and model trustworthiness is essential for its broader adoption. Future efforts should focus on enhancing data diversity, developing standardized ethical guidelines, and improving model transparency to ensure equitable and effective implementation in pediatric care.

Body dysmorphic disorder (BDD) is a psychiatric disorder characterized by a preoccupation with an imagined or slight defect which causes significant distress or impairment in functioning. Few studies have assessed gender differences in BDD in a non clinical population. Also no study assessed BDD in medical students. This study was designed to determine the point prevalence of BDD in Pakistani medical students and the gender differences in prevalence of BDD, body foci of concern and symptoms of BDD. The medical students enrolled in a medical university in Karachi, Pakistan filled out a self-report questionnaire which assessed clinical features of BDD. BDD was diagnosed according to the DSM-IV criteria. Out of the 156 students, 57.1% were female. A total of 78.8% of the students reported dissatisfaction with some aspect of their appearance and 5.8% met the DSM-IV criteria for BDD. The male to female ratio for BDD was 1.7. Regarding gender differences in body foci of concern, the top three reported foci of concern in male students were head hair (34.3%), being fat (32.8%), skin (14.9%) and nose(14.9%), whereas in females they were being fat (40.4%), skin (24.7%) and teeth (18%). Females were significantly more concerned about being fat (p = 0.005). Male students were significantly more concerned about being thin (p = 0.01) and about head hair (p = 0.012). BDD is fairly common in our medical student population, with a higher prevalence in males. Important gender differences in BDD symptomatology and reported body foci of concern were identified which reflected the influence of media on body image perception. The impact of cultural factors on the prevalence as well as gender differences in BDD symptomatology was also established.