JMIR Mental Health

Large language models (LLMs) now enable chatbots to engage in sensitive mental health conversations, including depression self-management. Yet their rapid deployment often overlooks how well these tools align with the priorities of people with lived experiences, which can introduce harms such as inaccurate information, lack of empathy, or inadequate crisis support.

Anecdotal evidence suggests that an increasing number of people are turning to generative artificial intelligence (GenAI) tools or artificial intelligence (AI)-assisted chatbots to discuss and manage mental health concerns. However, systematic data on the use and perception of such tools remain scarce.

Generative artificial intelligence (AI) chatbots have rapidly entered public use, including in contexts involving emotional support and mental health–related interactions. Although these systems are increasingly accessible, concerns have emerged regarding potential adverse psychiatric outcomes reported in public discourse, including psychosis, suicidal ideation, self-harm, and suicide. However, these reports largely originate from journalistic accounts rather than systematically verified clinical data.

Since 2020, Medicare Advantage (MA)–related internet searches have tripled, accompanied by increased regional marketing by private insurers. Commercial health insurance dominates the internet during enrollment periods, often outpacing public sources in accessibility. Prior studies suggest that MA advertising significantly shapes enrollment and may fuel choices over traditional Medicare in certain subpopulations. We sought to better understand how health plan marketing strategies affect consumers by using Google Trends data and MA health plan enrollment selection. We applied novel analysis to assess statistical relationships among marketing, internet searches, and enrollment data.

In recent years, advances in wearable sensor technology and artificial intelligence (AI) have provided new possibilities for detecting and monitoring depression.

Despite the potential of virtual reality (VR) for treatment and assessment in mental health care, its practical implementation remains limited. Much implementation research explores barriers and facilitators; fewer studies actually evaluate targeted implementation strategies and track how their effects evolve over time in mental health care practice.

The ubiquitous use of smartphones has given rise to maladaptive patterns of use, often termed “problematic smartphone use” (PSU), which disproportionately impacts children and young people and is associated with poor mental health. Emerging evidence suggests that patterns of smartphone use (eg, PSU and high smartphone screen time) may also influence eating patterns and contribute to symptoms associated with eating disorders (ED), although the nature of this relationship remains poorly understood.

Caregivers supporting individuals with Alzheimer disease and related dementias (AD/ADRD) frequently encounter prolonged emotional strain, psychological distress, and social isolation, yet their needs are largely overlooked in current technological and clinical interventions. The special routines and obligations of caregivers of individuals with AD/ADRD are frequently not well-suited to the many artificial intelligence–driven (AI-driven) mental health solutions that are currently available. This reveals a critical need for sophisticated, customized solutions created especially to help the mental health of caregivers for patients with AD/ADRD.

The prevalence of mental health conditions among young people is high and further increasing. Despite this considerable need, barriers remain to accessing and engaging with traditional mental health services. Online mental health peer support is increasingly popular among young people seeking help. However, research examining the effectiveness of online mental health peer support and user-centered experiences remains limited.

The benefits of physical activity (PA) for both physical and mental health, including major depressive disorder (MDD), are well established. Mobile devices, such as smartphones, offer a scalable way to monitor PA and its relationship with depressive symptoms in daily life.

The mechanisms through which mindfulness and third wave based digital programs exert their effects on stress reduction remain poorly understood. Identifying these mediators is essential to optimize their implementation, particularly in healthcare settings. This approach is particularly relevant for healthcare professionals, who are constantly exposed to high levels of emotional demands, work overload, and risk of burnout, especially in the aftermath of the COVID-19 pandemic. Despite the growing need for scalable and accessible mental health support in this population, such digital programs remain scarce and underutilized.

Patient safety incidents are a leading cause of harm in psychiatric settings, yet early warning systems (EWS) tailored to mental health remain underdeveloped. Traditional risk tools such as the Dynamic Appraisal of Situational Aggression–Inpatient Version (DASA-IV) offer limited predictive accuracy and are reactive rather than proactive.