2026· SHS Web of Conferences· 0 citations· 5 references
TL;DR
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
Mental health issues are a crisis for the world, with one out of eight individuals in low-income countries with a disorder experiencing treatment gaps. This paper presents a detailed overview of how Large Language Models (LLMs) and the Transformer architecture can address these problems. The development of conversational agents as rule-based systems to advanced models, which apply Cognitive-behavioral Therapy (CBT) in minimizing anxiety and depression, is examined. The review also looks at the use of LLMs in clinical screening such as multimodal depression and suicide risk. However, existing systems have enormous challenges despite the possibility of mental health assistance, such as “feigned empathy,” hallucinations, and relying on unimodal inputs using texts. To address these limitations that exist, a multimodal emotion-aware architecture, which pays attention to memory-enhanced personalization and emotion-specific reinforcement learning, is introduced. Finally, this review recommends hybrid human-AI approaches, which focus on safety and empathetic conversation to improve current mental health systems.
A targeted literature review and case-based analysis of 35 reported instances in which interactions with generative AI systems were temporally associated with the onset or worsening of psychotic symptoms revealed a conceptual hypothesis termed the delusional feedback loop, in which AI-generated responses iteratively validate distorted beliefs, contributing to their persistence and escalation.
N. Abesadze, Abigale Fernandes, Elizabeth Rubin· Cureus· 0 citations
Cognivia is an evidence-based artificial intelligence therapist that integrates automatic cognitive distortion identification and rational response generation and is proposed the first hierarchical quality evaluation framework for assessing LLM-generated rational responses, developed through collaboration between AI researchers and behavioral science experts.
Qi Chen, Siria Xiyueyao Luo, Jian Wang et al.· 0 citations
Despite recent advances in large language models (LLMs), their ability to generate empathetic mental health counseling responses in low-resource languages remains largely unexplored. To address this gap, we curate 625 authentic mental health cases from three complementary sources: (1) publicly available Facebook posts discussing mental health concerns, (2) transcripts from the Bangladeshi television program"Ami Akhon Ki Korbo", and (3) anonymized student questionnaire responses covering diverse emotional and psychological challenges. Based on these cases, we build an evaluation corpus comprising advice written by licensed clinical psychologists and responses generated by three modern proprietary LLMs: GPT-4o Mini, Claude 4.5 Haiku, and Gemini 2.5 Pro. We further propose the Role-Playing Reflective Chain-of-Thought Advisory Framework (RP-RCAF), a task-specific prompting strategy that combines expert-authored few-shot examples with structured self-reflection to produce supportive, culturally aware, and ethically aligned counseling through a compassionate advisor persona. We also introduce the Grok 4-Based Response Evaluation and Scoring Framework (G-REFS), which integrates automated assessment with expert psychologist validation across emotional sensitivity, cultural appropriateness, linguistic clarity, and ethical soundness. Experimental results show that RP-RCAF consistently outperforms conventional prompting across all evaluated models and produces responses that more closely align with professional psychological counseling.
Fatema Tuj Johora Faria, Mukaffi Bin Moin, Md. Mahfuzur Rahman et al.· 0 citations
A five-step clinical framework is proposed, operationalizable within the standard psychiatric encounter, to transform unguided AI use into a structured, supervised therapeutic tool and delineates clinically appropriate versus inappropriate uses of AI.
D. N. Moya-Sanchez· AI in Neuroscience· 0 citations
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.
Sara El Hajj, Ahmad Nsouli, M. Wehbe et al.· Frontiers in Psychiatry· 0 citations
EmoTrace, a multi-turn dialogue corpus generation framework centered on modeling seekers' emotional trajectories, is proposed, which outperforms existing approaches in terms of emotional richness and empathy quality.