Health

Mental Health · 48 stories · page 3 of 4

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Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance

Antimicrobial resistance is a constant threat to global public health, requiring innovative strategies for therapeutic target identification. Hence, this narrative review discusses the application of structural modeling and artificial intelligence in the functional prediction of proteins encoded by multidrug-resistant bacterial genomes. Tools such as AlphaFold and RoseTTAFold have enabled high-accuracy three-dimensional structure prediction, facilitating the annotation of hypothetical proteins and the identifica…

Journal of Computer-Aided Molecular Design

Evidence-backed gain

Metabolites stratify future major depressive disorder risk in obese population: a longitudinal machine learning analysis

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The evolving field of digital mental health: current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual reality

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Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives

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Cognitive offloading or cognitive overload? How AI alters the mental architecture of coping

Artificial intelligence (AI) has moved from being a specialized technological tool to an intimate presence in everyday life. Smart assistants organize our schedules, predictive systems anticipate our needs, and therapeutic chatbots promise to listen when no human is available (Zhang & Wang, 2024). The diffusion of AI into mental health care is often framed in highly optimistic terms: technologies that reduce stigma, democratize access, and provide affordable, always-on support (M & N, 2025;Sivasubramanian Balasu…

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A comprehensive review on application of cognitive behavioral therapy in emotional AI solutions for mental well-being

The integration of Cognitive Behavioral Therapy (CBT) into emotional AI systems has revolutionized the mental health care domain in detecting various issues. This paper reviews the evolution and application of AI agents that leverage CBT to provide personalized, accessible, and effective emotional support. By utilizing CBT methods like cognitive restructuring and guided self-reflection, the agents administer specific therapies that reduce negative thought patterns and enhance emotional responses. This paper outl…

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User perceptions and experiences of an AI-driven conversational agent for mental health support

Background: The increasing prevalence of artificial intelligence (AI)-driven mental health conversational agents necessitates a comprehensive understanding of user engagement and user perceptions of this technology. This study aims to fill the existing knowledge gap by focusing on Wysa, a commercially available mobile conversational agent designed to provide personalized mental health support. Methods: A total of 159 user reviews posted between January, 2020 and March, 2024, on the Wysa app's Google Play page we…

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“It happened to be the perfect thing”: experiences of generative AI chatbots for mental health

The global mental health crisis underscores the need for accessible, effective interventions. Chatbots based on generative artificial intelligence (AI), like ChatGPT, are emerging as novel solutions, but research on real-life usage is limited. We interviewed nineteen individuals about their experiences using generative AI chatbots for mental health. Participants reported high engagement and positive impacts, including better relationships and healing from trauma and loss. We developed four themes: (1) a sense of…

Health

The Impact of Chatbots on Adolescent Mental Health Development: A Comprehensive Literature Review

The integration of artificial intelligence (AI) chatbots in adolescent mental health care represents a transformative shift in how digital interventions address psychological well-being among young populations. This comprehensive review synthesizes evidence examining the multifaceted impact of chatbot technology on adolescent mental health development. A systematic search was conducted across PubMed, PsycINFO, Web of Science, and Scopus databases using terms related to chatbots, conversational agents, adolescent…

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Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers

Family caregivers of individuals with Alzheimer’s Disease and Related Dementia (AD/ADRD) face significant emotional and logistical challenges that place them at heightened risk for stress, anxiety, and depression. Although recent advances in generative AI—particularly large language models (LLMs)—offer new opportunities to support mental health, little is known about how caregivers perceive and engage with such technologies. To address this gap, we developed Carey, a GPT-4o–based chatbot designed to provide info…

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Advancements in machine learning and deep learning for early detection and management of mental health disorder

For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) have started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to improve clinical results significantly. However, they also present unique challenges relating to data integration and ethical issues. The development of ML and DL methods for the early diagnosis and treatment…

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Phenotyping antidepressant treatment response with deep learning in electronic health records

ABSTRACT Efficient, accurate phenotyping for antidepressant treatment response in electronic health records (EHRs) could facilitate precision psychiatry applications but remains a challenge. Increasingly, artificial intelligence methods using “deep learning” applied to clinical data have shown promise in complex classification problems. Here, we systematically evaluate the performance of eight deep-learning-based natural language processing models in classifying response to antidepressants in a large real-world…

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Exploring Students’ Perceptions and Usage of Artificial Intelligence in Supporting Mental Health: A Preliminary Study in Higher Education in Qatar

Background: Artificial intelligence (AI) is widely used in mental health care for screening, monitoring, and intervention. Notably, most studies of AI in mental health have been performed in Western contexts, with limited evidence from the Arab Gulf region, where cultural factors such as stigma, privacy, and help-seeking norms may influence acceptance. Objective: Investigating university students’ perceptions of AI in mental health support, including awareness, trust, readiness, and preferences in a Gulf context…

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Barriers and Facilitators to the Use of Large Language Model-Based Conversational Agents in Mental Healthcare: A Systematic Review

(1) Background/Objectives: Over one billion individuals globally live with mental health conditions, yet the treatment gap exceeds 75% in low- and middle-income countries. Large language model (LLM)-based conversational agents have emerged as a potentially scalable solution, though the evidence base remains nascent and largely pre-clinical. This review synthesises barriers and facilitators to their implementation in mental healthcare using the Consolidated Framework for Implementation Research (CFIR). (2) Method…

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Young people’s perceptions and recommendations for conversational generative artificial intelligence in youth mental health

Conversational generative artificial intelligence agents (or genAI chatbots) could benefit youth mental health, yet young people's perspectives remain underexplored. We examined the Mental health Intelligence Agent (Mia), a genAI chatbot originally designed for professionals in Australian youth services. Following co-design, 32 young people participated in online workshops exploring their perceptions of genAI chatbots in youth mental health and to develop recommendations for reconceptualising Mia for consumers a…