JMIR Formative Research

Social desirability bias, the tendency to respond in ways that present the respondent favorably, is well documented in human self-report and has recently been observed in large language models (LLMs) completing personality inventories. Whether it also affects LLM responses to validated health screening instruments, where LLMs are increasingly proposed as simulated patients or synthetic respondents, has not been examined.

Adolescent mental health needs remain largely unmet, particularly among youth in geographically underserved communities. Digital peer support is a promising approach to expanding access to mental health support, yet real-world evidence on engagement patterns and clinical outcomes remains limited.

The use of generative AI in research and clinical practice is rapidly expanding, offering unprecedented capabilities in pattern extraction and data analysis. However, its application raises critical concerns regarding the accuracy and reliability of the information provided.

AI technologies have considerable potential to support mental health practitioners in identifying depression symptoms. However, existing tools are often developed with limited practitioner involvement and evaluated primarily on technical performance, leaving questions of clinical relevance, ethical design, and practitioner trust insufficiently addressed.

Body dysmorphic disorder (BDD) is a chronic, impairing condition for which cognitive behavioral therapy, including exposure and response prevention (ERP), is the first-line treatment, but few clinicians are trained in BDD-specific ERP. Constructing personalized exposure hierarchies is time-intensive and requires clinical expertise, particularly for patients with absent insight who may hold appearance beliefs with delusional conviction. Generative AI (GenAI)–powered chatbots, such as ChatGPT, have shown utility for clinical content, but their capacity to produce safe, appropriate ERP hierarchies for BDD has not been examined.

Long wait times and specialist shortages delay autism spectrum disorder (ASD) diagnosis, often beyond the period when early intervention is most effective. Digital diagnostic tools may help increase diagnostic capacity by supporting clinicians in delivering timely autism evaluations within routine outpatient care.

The integration of eHealth solutions into routine health care remains challenging despite their potential to improve care quality and efficiency. Living labs (LLs) have emerged as an approach to addressing these challenges by enabling co-design and iterative testing of eHealth tools. LLs promote collaboration among patients, health care professionals, developers, and policy makers. However, systematic insights into their practical implementation remain limited.

As Taiwan enters a super-aged society, the prevalence of dementia continues to rise, highlighting the need for scalable, noninvasive approaches to identify behavioral alterations associated with cognitive decline. AI-based analysis of spontaneous affective facial responses has emerged as a potential digital phenotyping tool, but its feasibility for assessing cognitive performance remains underexplored.

Hypertension is a common chronic condition associated with substantial morbidity globally. In Jordan, many patients struggle with sustained medication adherence and self-management, highlighting a need for accessible care strategies. Low-cost platforms such as WhatsApp may support telehealth education and remote follow-up, although formative pilot data are needed to assess feasibility in this context.

Traditional brick-and-mortar pulmonary rehabilitation (PR) programs remain substantially underused, with only 2.7% of Medicare beneficiaries completing PR within 12 months following hospitalization for a COPD exacerbation. Given the critical role of PR in improving exercise tolerance, quality of life, and reducing readmissions, innovative delivery models are needed to address this massive utilization gap. Virtual PR platforms have emerged as a potential solution, offering scalable, and home-based rehabilitation.

There is evidence of rapid adoption of large language models (LLMs) in undergraduate medical education, with medical students using them to support their learning. This raises concerns about the accuracy of LLM-generated information that students are learning from. Retrieval-augmented generation (RAG), which grounds an LLM in curated materials, may be a strategy to improve the accuracy of this information.

Millions of people now use general-purpose large language models (LLMs) for mental health support, yet how these models respond to emotional disclosures and whether their responses functionally resemble established therapeutic practices remain uncharacterized.