Health · Mental Health

All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

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AI as a Therapist, Companion, and Romantic Partner: Emerging Roles, Benefits, and Risks for Mental Health in Participatory Medicine

The line between tool and companion was once obvious, but conversational AI is blurring it in ways few researchers anticipated. Large language model chatbots and purpose-built AI companion agents are now used by millions of people every day. They are not being used to simply retrieve information but, instead, to offer emotional support, help process personal distress, and sustain what many describe as genuine relationships. Research puts the scale of this shift in sharp relief as nearly half (48.7%) of individua…

Journal of Participatory Medicine · Health

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Trustworthy artificial intelligence for rural health care

Regional, rural and remote Australians experience poorer health outcomes and substantially higher rates of suicide and self-harm than those in major cities. Artificial intelligence could support earlier identification of distress, safer triage and more timely care alongside telehealth and clinical decision support, but only if it is treated as a health intervention with explicit safety nets and independent evaluation. We propose a minimum viable governance model, including Indigenous partnership, language safety…

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Evidence-backed gain

Local Recurrence Prediction After Carbon-ion Radiotherapy for Early-stage Non-small Cell Lung Cancer Using Machine Learning

Background/aim Predicting local recurrence remains challenging in carbon-ion radiotherapy (CIRT) for non-small cell lung cancer (NSCLC). In this study, we aimed to develop and validate a machine learning model to predict local recurrence after CIRT for early-stage peripheral NSCLC. Patients and methods We retrospectively analyzed patients treated with CIRT at our institution between 2010 and 2020. An Extreme Gradient Boosting classifier using clinical parameters was developed to predict local recurrence within 2…

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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…

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Evidence-backed gain

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

Background Obesity is a well-established risk factor for major depressive disorder (MDD), yet the risk is not uniform, highlighting the need for precise risk stratification. This study aimed to develop a metabolomics-based prediction model to identify high-risk metabolic phenotypes among obese participants and to elucidate the causal metabolic pathways involved. Methods Forty-one-thousand-four-hundred-fifty-nine obese participants were followed for a median of 14.4 years. We integrated multiple machine learning…

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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

The expanding domain of digital mental health is transitioning beyond traditional telehealth to incorporate smartphone apps, virtual reality, and generative artificial intelligence, including large language models. While industry setbacks and methodological critiques have highlighted gaps in evidence and challenges in scaling these technologies, emerging solutions rooted in co-design, rigorous evaluation, and implementation science offer promising pathways forward. This paper underscores the dual necessity of ad…

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

Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI's role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemedicine, and wearab…

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

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

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…

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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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