Extreme Weather, Vulnerable Populations, and Mental Health: The Timely Role of AI Interventions DOI Open Access
Mehak Batra, Bircan Erbas

International Journal of Environmental Research and Public Health, Journal Year: 2025, Volume and Issue: 22(4), P. 602 - 602

Published: April 11, 2025

Environmental disasters are becoming increasingly frequent and severe, disproportionately impacting vulnerable populations who face compounded risks due to intersectional factors such as gender, socioeconomic status, rural residence, cultural identity. These events exacerbate mental health challenges, including post-traumatic stress disorder (PTSD), anxiety, depression, particularly in low- middle-income countries (LMICs) underserved areas of high-income (HICs). Addressing these disparities necessitates inclusive, culturally competent, intersectional, cost-effective strategies. Artificial intelligence (AI) presents transformative potential for delivering scalable tailored interventions that account vulnerabilities. This perspective highlights the importance co-designing AI tools with at-risk populations, integrating solutions into disaster management frameworks, ensuring their sustainability through research, training, policy support. By embedding resilience climate adaptation strategies, stakeholders can foster equitable recovery reduce long-term burden environmental disasters.

Language: Английский

Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures DOI Open Access
Brian A. Zaboski, Lora Bednarek

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(7), P. 2442 - 2442

Published: April 3, 2025

Obsessive-compulsive disorder (OCD) is a complex psychiatric condition characterized by significant heterogeneity in symptomatology and treatment response. Advances neuroimaging, EEG, other multimodal datasets have created opportunities to identify biomarkers predict outcomes, yet traditional statistical methods often fall short analyzing such high-dimensional data. Deep learning (DL) offers powerful tools for addressing these challenges leveraging architectures capable of classification, prediction, data generation. This brief review provides an overview five key DL architectures-feedforward neural networks, convolutional recurrent generative adversarial transformers-and their applications OCD research clinical practice. We highlight how models been used the predictors response, diagnose classify OCD, advance precision psychiatry. conclude discussing implementation DL, summarizing its advances promises underscoring field.

Language: Английский

Citations

0
Extreme Weather, Vulnerable Populations, and Mental Health: The Timely Role of AI Interventions DOI Open Access
Mehak Batra, Bircan Erbas

International Journal of Environmental Research and Public Health, Journal Year: 2025, Volume and Issue: 22(4), P. 602 - 602

Published: April 11, 2025

Environmental disasters are becoming increasingly frequent and severe, disproportionately impacting vulnerable populations who face compounded risks due to intersectional factors such as gender, socioeconomic status, rural residence, cultural identity. These events exacerbate mental health challenges, including post-traumatic stress disorder (PTSD), anxiety, depression, particularly in low- middle-income countries (LMICs) underserved areas of high-income (HICs). Addressing these disparities necessitates inclusive, culturally competent, intersectional, cost-effective strategies. Artificial intelligence (AI) presents transformative potential for delivering scalable tailored interventions that account vulnerabilities. This perspective highlights the importance co-designing AI tools with at-risk populations, integrating solutions into disaster management frameworks, ensuring their sustainability through research, training, policy support. By embedding resilience climate adaptation strategies, stakeholders can foster equitable recovery reduce long-term burden environmental disasters.

Language: Английский

Citations

0