There is potential for digital mental health interventions to provide affordable, efficient, and scalable support to individuals. Digital interventions, including cognitive behavioral therapy, stress management, and mindfulness programs, have shown promise when applied in workplace settings.

Self-guided internet-delivered cognitive behavioral therapy (ICBT) achieves greater reach than ICBT delivered with therapist guidance, but demonstrates poorer engagement and fewer clinical benefits. Alternative models of care are required that promote engagement and are effective, accessible, and scalable.

Previous studies have found that psychotic disorders are among the most stigmatized mental disorders. Of note, virtual reality (VR) interventions have been associated with improvements in attitudes and empathy and reduced stigma toward individuals with psychotic disorders, especially among undergraduates, but this has not been examined among mental health care professionals.

Generative artificial intelligence (GenAI) shows potential for personalized care, psychoeducation, and even crisis prediction in mental health, yet responsible use requires ethical consideration and deliberation and perhaps even governance. This is the first published theme issue focused on responsible AI in mental health. It brings together evidence and insights on GenAI’s capabilities—such as emotion recognition, therapy-session summarization, and risk assessment—while highlighting the sensitive nature of mental health data and the need for rigorous validation. Contributors discuss how bias, alignment with human values, transparency, and empathy must be carefully addressed to ensure ethically grounded AI-assisted care. By proposing conceptual frameworks, best practices, and regulatory approaches, including ethics of care and the preservation of socially important humanistic elements, this theme issue underscores that GenAI can complement, rather than replace, the vital role of human empathy in clinical settings. To achieve this, an ongoing collaboration between researchers, clinicians, policymakers, and technologists is essential.

Digital mental health interventions (DMHIs) to monitor and improve the health of people with psychosis or bipolar disorder show promise; however, user engagement is variable, and integrated clinical use is low.

Although suicide bereavement is highly distressing and is associated with an increased risk of suicidal behaviors and mental and physical health impairments, those bereaved by suicide encounter difficulties accessing support. Digital resources offer new forms of support for bereaved people. However, digital resources dedicated to those bereaved by suicide are still limited.

The use of natural language processing (NLP) in mental health research is increasing, with a wide range of applications and datasets being investigated.

Fully-automated digital interventions delivered via smartphone apps have proven efficacious for a wide variety of mental health outcomes. An important value is that they are accessible at a low cost, thereby increasing their potential public impact and reducing disparities. However, a major challenge to their successful implementation is the phenomenon of users dropping out early.

Digital wearable devices, worn on or close to the body, have potential for passively detecting mental and physical health symptoms among people with severe mental illness (SMI); however, the roles of consumer-grade devices are not well understood.

Digital interventions typically involve using smartphones or PCs to access online or downloadable self-help and may offer a more accessible and convenient option than face-to-face interventions for some people with mild to moderate eating disorders. They have been shown to substantially reduce eating disorder symptoms, but treatment dropout rates are higher than for face-to-face interventions. We need to understand user experiences and preferences for digital interventions to support the design and development of user-centered digital interventions that are engaging and meet users’ needs.

Digital health technologies are increasingly being integrated into mental health care. However, the adoption of these technologies can be influenced by patients’ digital literacy and attitudes, which may vary based on sociodemographic factors. This variability necessitates a better understanding of patient digital literacy and attitudes to prevent a digital divide, which can worsen existing health care disparities.

The rise of wearable sensors marks a significant development in the era of affective computing. Their popularity is continuously increasing, and they have the potential to improve our understanding of human stress. A fundamental aspect within this domain is the ability to recognize perceived stress through these unobtrusive devices.