AI for Brain, Cognitive, and Mental Wellbeing

One of the biggest challenges in modern public health is improving brain, cognitive, and mental well-being. Despite decades of multidisciplinary efforts, the global burden of mental and cognitive disorders continues to rise, affecting individuals across all demographic groups. Many of these conditions lack objective biomarkers, rely on subjective clinical assessments, and are often complicated by stigma or limited patient engagement. These complexities present unique and persistent barriers to diagnosis, treatment, and long-term management.Recent breakthroughs in artificial intelligence (AI), alongside the increasing availability of large-scale datasets, offer new opportunities to address these challenges. AI-powered models have demonstrated the ability to analyze a wide range of complex, multi-modal data: from electronic health records and neuroimaging to genetic profiles and behavior patterns. These tools can be utilized for early detection of cognitive decline and other brain or mental disorders, prediction of treatment responses, and continuous remote monitoring in real-world settings. However, concerns about algorithmic bias, generalizability, data privacy, and ethical deployment remain as critical issues. In addition, the adoption of AI in brain and mental health raises important questions about economic value, sustainability, and the incentives shaping innovation and implementation within healthcare systems.Therefore, this Research Topic seeks to explore how recent advancements in AI can be applied to support brain and mental health, as well as to address the growing urgency to evaluate their real-world impact, clinical usefulness, ethical boundaries, and economic implications. The goal of the Research Topic is to create an interdisciplinary platform to advance dialogue on the application, promise, and limitations of AI in brain, cognitive, and mental well-being.We invite contributions that focus on, but are not limited to, the following themes:• Development and validation of AI models for early detection of risk groups, personalized intervention, and treatment optimization;• Integration of multi-modal data in brain, cognitive, and mental health, such as clinical datasets, neuroimaging, genomics data, and behavioral data;• Bias, fairness, and inclusivity in mental health AI;• Ethical and regulatory considerations in brain and mental health AI;• AI applications for population-level mental health and policy;• Economic evaluation of AI-driven innovations in brain and mental health, including cost-effectiveness and healthcare resource allocation. Topic editors: Ayan Paul, Hyunju Kim, Tannista Banerjee Source: https://www.frontiersin.org/research-topics/71976/ai-for-brain-cognitive-and-mental-wellbeing