Sustainability of AI-Assisted Mental Health Intervention: A Review of the Literature from 2020–2025

Danicsa Karina Espino Carrasco (), María del Rosario Palomino Alcántara, Carmen Graciela Arbulú Pérez Vargas, Briseidy Massiel Santa Cruz Espino, Luis Jhonny Dávila Valdera, Cindy Vargas Cabrera, Madeleine Espino Carrasco, Anny Dávila Valdera and Luz Mirella Agurto Córdova
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Danicsa Karina Espino Carrasco: School of Nursing, Faculty of Health Sciences, Universidad César Vallejo, Chiclayo 14000, Peru
María del Rosario Palomino Alcántara: School of Nursing, Faculty of Health Sciences, Universidad Particular de Chiclayo, Chiclayo 14000, Peru
Carmen Graciela Arbulú Pérez Vargas: School of Nursing, Faculty of Health Sciences, Universidad César Vallejo, Chiclayo 14000, Peru
Briseidy Massiel Santa Cruz Espino: School of Nursing, Faculty of Health Sciences, Universidad Señor de Sipán, Chiclayo 14000, Peru
Luis Jhonny Dávila Valdera: School of Nursing, Faculty of Health Sciences, Universidad Nacional Mayor de San Marcos, Lima 00051, Peru
Cindy Vargas Cabrera: School of Nursing, Faculty of Health Sciences, Universidad Señor de Sipán, Chiclayo 14000, Peru
Madeleine Espino Carrasco: School of Nursing, Faculty of Health Sciences, Universidad César Vallejo, Chiclayo 14000, Peru
Anny Dávila Valdera: School of Nursing, Faculty of Health Sciences, Universidad Nacional Mayor de San Marcos, Lima 00051, Peru
Luz Mirella Agurto Córdova: School of Nursing, Faculty of Health Sciences, Universidad César Vallejo, Chiclayo 14000, Peru

IJERPH, 2025, vol. 22, issue 9, 1-33

Abstract: This systematic review examines the role of artificial intelligence (AI) in the development of sustainable mental health interventions through a comprehensive analysis of literature published between 2020 and 2025. In accordance with the PRISMA guidelines, 62 studies were selected from 1652 initially identified records across four major databases. The results revealed four dimensions critical for sustainability: ethical considerations (privacy, informed consent, bias, and human oversight), personalization approaches (federated learning and AI-enhanced therapeutic interventions), risk mitigation strategies (data security, algorithmic bias, and clinical efficacy), and implementation challenges (technical infrastructure, cultural adaptation, and resource allocation). The findings demonstrate that long-term sustainability depends on ethics-driven approaches, resource-efficient techniques such as federated learning, culturally adaptive systems, and appropriate human-AI integration. The study concludes that sustainable mental health AI requires addressing both technical efficacy and ethical integrity while ensuring equitable access across diverse contexts. Future research should focus on longitudinal studies examining the long-term effectiveness and cultural adaptability of AI interventions in resource-limited settings.

Keywords: artificial intelligence; sustainable mental health; ethics; resource efficiency; personalization; cultural adaptation; human-AI integration (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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