Journal of Medical Internet Research

Depressive disorders are one of the most prevalent psychiatric disorders globally and impose considerable individual and societal burdens. Psychotherapy, including cognitive behavioral therapy, is recommended as a first-line treatment especially for mild to moderate depressive disorders. However, face-to-face psychotherapy is often limited by issues of accessibility and cost. Digital therapeutics (DTx)—defined as health software intended to treat or alleviate a disease, disorder, condition, or injury by generating and delivering a medical intervention that has a demonstrable positive therapeutic impact on a patient by the International Organization for Standardization—have gained increasing attention as alternatives for overcoming these hurdles. With advances in digital technology, digital placebos have begun to be used as comparators in the clinical trials of DTx to assess the effects of active interventions accurately. However, the characteristics of clinical trials, the magnitude of digital placebos effects, and their moderators remain poorly understood.

Generative artificial intelligence (GAI) chatbots are frequently used for mental health–related questions, including suicide-related queries. User interfaces (UIs) may include additional safety controls not present in direct application programming interface (API) access (eg, moderation rules that flag or block harmful content, crisis refusal templates, or system-level instructions).

HIV/AIDS stigma remains a major barrier to public health intervention and social inclusion. In China, short-video platforms, such as Douyin, have become important spaces where people living with HIV/AIDS share daily-life narratives and receive public responses. However, little is known about how public responses to these narratives vary geographically, or how regional sociodemographic and epidemiological contexts are associated with digital stigma and support.

Limited recent literature has evaluated the use of large language models (LLMs) in the qualitative analysis of health data; more research is needed to expand the generalizability of LLM use and to evaluate potential ethical considerations.

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children, characterized by core symptoms of hyperactivity, impulsivity, and inattention, in addition to cognitive impairments that compromise physical and psychological development. Digital technology–based interventions have emerged as a promising approach for ameliorating both core symptoms and cognitive impairments. However, a comprehensive evidence base supporting their efficacy is lacking.

This position paper introduces young carers as a key stakeholder within AI. Research on AI for health care endeavors to assist and optimize current health care services; yet, young carers have been neglected thus far. Accordingly, this paper situates itself at the intersection of young people under the age of 18 years old providing care (referred to as young carers) and AI research. Through a socio-legal and public health lens, this paper (1) situates young carers to showcase (2) why they must be accounted for in AI systems, and (3) how AI researchers can do so. This lens highlights the difficulties young carers tend to face at present and how AI systems can alleviate those—rather than reproduce them. Accordingly, we rely on the norms-in-the-loop framework to demonstrate why the AI community must account for young carers in AI systems. We first synthesize research on both young carers and AI research on contextual, legal, and social norms to then propose practical ways to do so through design, datasets, and in situ personalization. Overall, this paper offers a critical foundation for AI researchers to include young carers in the development of AI systems at various levels: from institutions to care receivers, and to young carers themselves. Ultimately, this paper posits that young carers must be identified and supported, which demands their inclusion in AI research to understand how best to do so. Accounting for young carers will allow for their unique perspectives to be reflected in AI systems and help them thrive beyond their care responsibilities.

The pharmaceutical industry faces unprecedented challenges, including declining drug approval rates, plummeting research and development returns, and escalating protocol complexity. Digital transformation represents the most promising end-to-end approach for addressing these challenges, yet there is limited understanding of industry-wide priorities and implementation barriers.

Childhood anxiety disorders are common and lead to substantial negative long-term outcomes in various life domains. Although digital interventions for preventing childhood anxiety problems have been increasingly used in the recent decade, there is limited evidence documenting their cost-effectiveness (CE).

Aphasia, an acquired language disorder that affects the ability to understand and produce language, significantly impairs effective communication. Large language models (LLMs) such as ChatGPT may help by generating fluent and coherent text, offering new ways to support communication for people with aphasia.

As the population of survivors of cancer grows and new information technologies become widespread in Hong Kong, survivors of cancer increasingly seek ongoing care and support information from large language models (LLMs). While these tools provide immediate conversational responses, they carry substantial risks of generating inaccurate, unsafe, or generic medical advice. Evaluating LLM response performance is particularly critical in Hong Kong’s bilingual health care context.

Carotid ultrasound is traditionally confined to specialist settings because examination quality and diagnostic reliability are highly dependent on the operator’s technical skill and experience. Recent advances in AI-assisted ultrasound may enable task shifting to primary care staff and support the integration of personalized visual risk communication into routine preventive care. Visualization of subclinical atherosclerosis has been shown to strengthen cardiovascular risk communication and support preventive engagement. However, limited evidence exists on how such technology can be practically integrated into routine primary care workflows and what conditions are required to support use by nonexpert operators.

Theoretical perspectives offer contrasting predictions regarding how inequalities in health technology adoption evolve over time. While classical diffusion of innovations theory suggests that early adoption gaps may narrow as technologies such as telemedicine become widespread, the inverse equity hypothesis posits that such disparities are likely to persist or even widen. Longitudinal evidence on the long-term evolution of individual telemedicine adoption, however, remains scarce.