MACHINE LEARNING IN PEDIATRIC DISEASE DIAGNOSIS: EVIDENCE, APPLICATIONS, AND FUTURE DIRECTIONS (ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ, 2026)
Pediatric diseases present unique diagnostic challenges due to age-dependent symptom variability, limited verbal communication from patients, and the necessity of avoiding invasive procedures. Machine learning (ML) and deep learning (DL) technologies have increasingly demonstrated potential for transforming diagnostic
Keywords: Pediatric medicine encompasses the diagnosis and management of diseases from birth through adolescence, a period characterized by rapid physiological development, evolving immunological competence, and age-specific pathophysiology [1, 4]. Globally, infectious diseases, nutritional deficiencies, congenital anomalies, and malignancies collectively account for the majority of childhood morbidity and mortality, with disproportionate impact in low- and middle-income countries [3]. Accurate and timely diagnosis in pediatric populations is essential for initiating appropriate therapy, preventing complications, and reducing unnecessary antibiotic and medication use, yet remains challenged by the nonspecific presentation of many childhood illnesses and constraints on invasive investigation [2, 5].