Editorial: AI innovations in neurological and psychiatric disorder management: diagnosis to treatment

Neurological and psychiatric disorders affect over 1 billion people globally, posing immense health, economic, and social burdens. From neurodevelopmental conditions (ADHD, dyslexia) to cerebrovascular events (stroke), age-related cognitive impairment (MCI), and traumatic brain injury (TBI), these disorders share core challenges: complex pathogenesis, heterogeneous presentations, delayed diagnosis, and limited personalized care. Traditional subjective assessments and one-size-fitsall interventions often yield suboptimal outcomes, creating an urgent need for innovative solutions.Artificial intelligence (AI)-encompassing machine learning, deep learning, and data analyticshas emerged as a transformative tool, harnessing multi-modal data (neuroimaging, clinical metrics, behavioral assessments) to enable precise pattern recognition, predictive modeling, and adaptive interventions. This Research Topic gathers five cutting-edge studies translating AI advancements into clinical practice across diverse brain disorders. Below, we synthesize their core contributions, contextualizing their significance in advancing AI-driven brain health.Artificial intelligence in ADHD: a global perspective on research hotspots, trends and clinical applications (Wang et al., 2025) The five studies in this Research Topic collectively showcase the breadth and depth of AI's potential to transform neurological and psychiatric care. From mapping global research trends in ADHD and optimizing stroke care pathways, to enabling early MCI screening, personalizing TBI management, and advancing dyslexia interventions, each contribution aligns with the Topic's core mission: leveraging datadriven innovation to address unmet clinical needs. Together, they highlight three key themes: the power of multi-modal data integration, the importance of personalized care, and the need to bridge researchpractice gaps for equitable AI translation.