Overall, findings suggest that current systems remain insufficiently adapted to the MENA context, underscoring the need for culturally grounded, dialect-sensitive, and clinically supervised approaches to ensure safe and effective integration.
Abstract
Mental health disorders represent a growing burden across the Middle East and North Africa (MENA) region, where depression and anxiety are highly prevalent amid conflict, displacement, and socioeconomic strain, affecting up to 40 percent of adults, yet treatment gaps remain at 80-95% due to provider shortages, financial strain, and cultural barriers. In this context, artificial intelligence (AI), in the form of large language models (LLMs) and specialized psychotherapy chatbots, may offer a scalable adjunct to help address these gaps through anonymous screening, predictive risk modeling, psychoeducation, and brief interventions. This narrative review examines current evidence of AI-driven conversational tools in mental health with a specific focus on their application, acceptance, and limitations within the MENA region. To do so, A structured search of MEDLINE and Embase (2000–2026) identified studies on conversational AI in mental health, prioritizing evidence from the MENA region and supplemented by relevant global literature. Overall, findings suggest that while these tools offer high accessibility and user engagement, particularly for low-intensity support, their effectiveness is limited by linguistic and cultural mismatches, including Arabic diglossia and poor alignment with locally grounded expressions of distress. At the same time, user acceptance reflects a paradox in which stigma and privacy concerns drive reliance on anonymous AI tools while simultaneously limiting trust in their clinical reliability, reinforcing a preference for hybrid models with human oversight. Taken together, these findings indicate that current systems remain insufficiently adapted to the MENA context, underscoring the need for culturally grounded, dialect-sensitive, and clinically supervised approaches to ensure safe and effective integration.
Mental illness represents a growing global health concern, with increasing prevalence and burden across populations. Timely diagnosis and effective treatment are essential to improving individual health outcomes. In this context, artificial intelligence (AI) offers significant potential through rapid and precise data analysis. This narrative literature review aims to examine the application of AI technologies, specifically natural language processing (NLP), machine learning (ML), and predictive modelling, in the diagnosis and therapy of mental health disorders among adult clinical populations. The review includes twelve peer-reviewed articles indexed in Scopus between 2014 and 2024, selected based on relevance, methodological rigor, and contribution to the field. Findings were organised into three thematic clusters: (1) AI-assisted diagnostic accuracy and predictive modelling (e.g., fMRI-based PTSD prediction, depression detection via multimodal neural networks); (2) AI-enhanced therapeutic delivery and user engagement (e.g., AI-assisted online social therapy, remotely supervised brain stimulation); and (3) ethical, privacy, and implementation challenges (e.g., data bias, lack of transparency, and population representativeness). These technologies show promise in reducing human error and enhancing mental health care delivery; however, persistent challenges include data privacy, ethical considerations, and the need for diverse, large-scale datasets. Future research should focus on developing standardised implementation protocols using frameworks such as PRISMA, ensuring population diversity, and addressing ethical safeguards. Collaboration among mental health professionals, AI technologists, and policymakers is essential to promote safe, effective, and equitable integration of AI in psychological diagnosis and therapy.
Juster Donal Sinaga· Journal of Society Counselin...· 0 citations
The world is changing rapidly in the twenty-twenties due to the growth of Artificial Intelligence (AI). Generative AI and large language model chatbots are different types of AI that have quickly diffused into everyday life and psychological practice. The existence of unmet mental health requirements is what makes some members of the general population deem AI chatbots as an alternative to talk therapy. At the same time, AI-based therapeutic tools are integrated into the clinical decision support system, a web-based application, and professional education. This literature review discusses empirical studies and case studies of AI in the psychology field. The findings indicate that AI is potentially able to stimulate accessibility, engagement, and temporary relief of symptoms. Nevertheless, accidents are severe, most importantly, the chances of giving wrong answers in high-stakes situations. Reports of harmful crisis responses from chatbots demonstrate the limitations of AI as a replacement for human judgment or therapeutic relationships. Future research should focus on long-term outcomes and safeguards to ensure safe, transparent integration into mental health care.
Cognitive and mental health (CMH) disorders are increasingly prevalent worldwide and pose significant societal, clinical, and economic challenges. While conventional mental health support methods remain limited by scalability and reactivity, recent advances in artificial intelligence have opened new opportunities for scalable, proactive, and personalized mental health support. Rapid progress has been made in areas such as mental health assessment, empathetic conversational agents, and AI-assisted psychological interventions; however, these efforts remain fragmented across disciplines, and critical challenges related to reliability, interpretability, ethics, and real-world deployment persist. To address these gaps, we propose the International Workshop on AI for Cognitive and Mental Health Support (AI4Mental), a half-day interdisciplinary forum that brings together researchers and practitioners from data mining, machine learning, NLP, HCI, healthcare, and social sciences. The workshop focuses on three complementary pillars: AI as Assessment, AI as Emotional Support, and AI as Psychological Intervention, covering topics ranging from multimodal mental health detection and longitudinal risk modeling to empathetic dialogue systems and responsible interventions. By consolidating emerging research and fostering cross-disciplinary dialogue, AI4Mental aims to advance trustworthy, effective, and socially responsible AI solutions for cognitive and mental health support, aligning closely with SIGKDD's mission on data science for social good.
Xiang-jian Wang, Haoyang Li, Chen Li et al.· Proceedings of the 32nd ACM...· 0 citations
A multimodal emotion-aware architecture, which pays attention to memory-enhanced personalization and emotion-specific reinforcement learning, is introduced and hybrid human-AI approaches, which focus on safety and empathetic conversation to improve current mental health systems are recommended.
Abstract Digital mental health has become an established part of mental health care, but the rapid arrival of large language models and other artificial intelligence (AI) tools has refocused attention on the evidence needed to guide the field. This editorial updates the research priorities articulated by JMIR Mental Health in 2023, while reaffirming their emphasis on equity, replicability, privacy, efficacy, and engagement. While the importance of these priorities has not changed in recent years, the urgency with which they must now be applied has. As digital tools become more clinically consequential, research must move beyond demonstrating that a technology is feasible, usable, or novel. The field now needs studies that clarify how these tools work, for whom they are beneficial, under what conditions they may cause harm, and how they can be ethically integrated into care. We call for research that is transparent about the technologies being studied, grounded in meaningful clinical questions, attentive to safety, and designed to produce knowledge that remains useful as specific products and models change.
M. Birk, Shruti Kochhar, Keris Myrick et al.· JMIR Mental Health· 1 citation
A list of eight best practices was created to assist developers with designing AI systems in a way that would reduce the overall risk of harm for users attempting to use their AI for mental health cases.
Joshua Frankenfield, Briana M. Sobel, Barbara Chaparro· Proceedings of the Internati...· 0 citations