Background/Objectives: Mental health disorders among children and adolescents represent a significant global health challenge, with approximately 50% of conditions emerging before age 14. Despite substantial investment in services, persistent barriers such as provider shortages, stigma, and accessibility issues continue to limit effective care delivery. This narrative review examines the emerging application of conversational artificial intelligence (AI) in pediatric mental health contexts, mapping the current evidence base, identifying therapeutic mechanisms, and exploring unique developmental considerations required for implementation. Methods: We searched multiple electronic databases (PubMed/MEDLINE, PsycINFO, ACM Digital Library, IEEE Xplore, and Scopus) for literature published between January 2010 and February 2025 that addressed conversational AI applications relevant to pediatric mental health. We employed a narrative synthesis approach with thematic analysis to organize findings across technological approaches, therapeutic applications, developmental considerations, implementation contexts, and ethical frameworks. Results: The review identified promising applications for conversational AI in pediatric mental health, particularly for common conditions like anxiety and depression, psychoeducation, skills practice, and bridging to traditional care. However, most robust empirical research has focused on adult populations, with pediatric applications only beginning to receive dedicated investigation. Key therapeutic mechanisms identified include reduced barriers to self-disclosure, cognitive change, emotional validation, and behavioral activation. Developmental considerations emerged as fundamental challenges, necessitating age-appropriate adaptations across cognitive, emotional, linguistic, and ethical dimensions rather than simple modifications of adult-oriented systems. Conclusions: Conversational AI has potential to address significant unmet needs in pediatric mental health as a complement to, rather than replacement for, human-delivered care. Future research should prioritize developmental validation, longitudinal outcomes, implementation science, safety monitoring, and equity-focused design. Interdisciplinary collaboration involving children and families is essential to ensure these technologies effectively address the unique mental health needs of young people while mitigating potential risks.

Background/Objectives: Neurodegenerative diseases represent a growing global health challenge with limited therapeutic options. Physical exercise has emerged as a promising non-pharmacological intervention with potential neuroprotective effects. This narrative review examines the mechanisms through which exercise induces neuroplasticity and their implications for neurodegenerative disease prevention. Methods: We synthesized evidence from molecular, animal, and human studies on exercise-induced neuroplasticity and neurodegenerative disease prevention through a comprehensive literature review. Results: Exercise enhances neuroplasticity through multiple pathways: (1) neurotrophic signaling (BDNF, IGF-1, VEGF), (2) neuroendocrine regulation, (3) epigenetic modifications, and (4) metabolic pathway optimization. These molecular changes support structural adaptations including hippocampal neurogenesis, enhanced synaptic plasticity, improved cerebrovascular function, and optimized brain network connectivity. Exercise directly impacts pathological features of neurodegenerative diseases by reducing protein aggregation, attenuating excitotoxicity and oxidative stress, and enhancing mitochondrial function. Clinical evidence consistently demonstrates associations between physical activity and reduced neurodegenerative risk, with intervention studies supporting causal benefits on cognitive function and brain structure. Conclusions: Exercise represents a multi-target intervention addressing several pathological mechanisms simultaneously across various neurodegenerative conditions. Its accessibility, minimal side effects, and multiple health benefits position it as a promising preventive strategy. Future research should focus on understanding individual response variability, developing sensitive biomarkers, and creating personalized exercise prescriptions for optimal neuroprotection.

Background/Objectives: Neurodegenerative diseases represent a growing global health challenge with limited therapeutic options. Physical exercise has emerged as a promising non-pharmacological intervention with potential neuroprotective effects. This narrative review examines the molecular, structural, and functional mechanisms through which exercise induces neuroplasticity and their implications for neurodegenerative disease prevention. Methods: This narrative review synthesized evidence from molecular, animal, and human studies on exercise-induced neuroplasticity and neurodegenerative disease prevention. We conducted a comprehensive literature search of peer-reviewed publications spanning molecular mediators, structural and functional adaptations, neuroimmune pathways, direct effects on pathological features, clinical and epidemiological evidence, and translational considerations. Results: Exercise induces neuroplasticity through multiple complementary pathways, including enhanced neurotrophic factor signaling, optimized neuroendocrine responses, epigenetic modifications, and improved metabolic signaling. These molecular changes support structural and functional adaptations, including hippocampal neurogenesis, enhanced synaptic plasticity, improved cerebrovascular function, and optimized network connectivity. Exercise directly impacts pathological features of neurodegenerative diseases by reducing protein aggregation, attenuating excitotoxicity and oxidative stress, and enhancing mitochondrial function. Clinical evidence consistently demonstrates associations between physical activity and reduced neurodegenerative risk, with intervention studies supporting causal benefits on cognitive function and brain structure. Conclusions: Exercise represents a multi-target intervention that addresses several pathological mechanisms simultaneously across various neurodegenerative conditions. Optimizing preventive efficacy requires consideration of exercise parameters, individual characteristics, and implementation strategies. Future research should focus on mechanistic gaps, biomarker development, and precision approaches that tailor interventions to individual risk profiles. Exercise's accessibility, minimal side effects, and multiple health benefits position it as a promising strategy to reduce the burden of neurodegenerative disease.

Background/Objectives: Mental health disorders among children and adoles-cents represent a significant global health challenge, with approximately 50% of condi-tions emerging before age 14. Despite substantial investment in services, persistent barriers including provider shortages, stigma, and accessibility issues limit effective care delivery. This scoping review examines the emerging application of conversational ar-tificial intelligence (AI) in pediatric mental health contexts, mapping the current evidence base, identifying therapeutic mechanisms, and exploring unique developmental con-siderations required for implementation. Methods: Following established scoping review methodology, we searched multiple electronic databases (PubMed/MEDLINE, PsycINFO, ACM Digital Library, IEEE Xplore, and Scopus) for literature published between January 2010 and February 2025 that addressed conversational AI applications relevant to pedi-atric mental health. We employed a narrative synthesis approach with thematic analysis to organize findings across technological approaches, therapeutic applications, devel-opmental considerations, implementation contexts, and ethical frameworks. Results: The review identified promising applications for conversational AI in pediatric mental health, particularly for common conditions like anxiety and depression, psychoeducation, skills practice, and bridging to traditional care. However, most robust empirical research has focused on adult populations, with pediatric applications only beginning to receive dedicated investigation. Key therapeutic mechanisms identified include reduced barriers to self-disclosure, cognitive change, emotional validation, and behavioral activation. Developmental considerations emerged as fundamental challenges, necessitating age-appropriate adaptations across cognitive, emotional, linguistic, and ethical dimen-sions rather than simple modifications of adult-oriented systems. Conclusions: Conver-sational AI has potential to address significant unmet needs in pediatric mental health as a complement to, rather than replacement for, human-delivered care. Future research should prioritize developmental validation, longitudinal outcomes, implementation science, safety monitoring, and equity-focused design. Interdisciplinary collaboration involving children and families is essential to ensure these technologies effectively ad-dress the unique mental health needs of young people while mitigating potential risks.