Journal of Medical Internet Research

Affirming care for lesbian, gay, bisexual, transgender, queer, and other individuals with diverse sexual orientations and gender identities (LGBTQ+) populations refers to culturally and clinically competent health care that recognizes specific health needs and provides respectful, inclusive, equitable, and nondiscriminatory services that are supportive of diverse identities. LGBTQ+ populations face greater discrimination in health care, leading to higher levels of unmet health needs than the general population. Very few primary care practices in the United States have training for staff and clinicians on LGBTQ+ health care needs. Despite the growing need for LGBTQ+ affirming care, there are no national standards or requirements for LGBTQ+ cultural competence training for primary health care providers in the United States.

Personalized dietary counseling is central to recurrence prevention in patients with urolithiasis, particularly after a 24-hour urine metabolic evaluation. However, translating quantitative metabolic abnormalities into patient-facing, guideline-concordant, and safe dietary recommendations can be challenging in routine clinical practice. Large language models (LLMs) may assist with this task, but unguided responses may overlook key metabolic priorities or case-specific safety constraints.

Federal and state school nutrition policies over the past 20 years have improved school meal nutritional quality, children’s diet quality, and childhood obesity prevalence in the United States. However, increasing use of mobile food delivery apps during school hours may introduce new dietary risks among adolescents.

In burn care, one of the most debated topics is the optimal treatment of patients with deep partial-thickness burns. With these patients, the decision must be made to perform early surgery or to wait and potentially limit, or even avoid, surgery. Both options are available in Dutch burn care, and the best treatment option is decided on clinical outcomes as well as patients’ preferences. This complexity highlights the need for shared decision-making (SDM) and a decision aid (DA) to facilitate this process.

Digital health applications generate rich behavioral data; yet, how users transition between behavioral states remains poorly understood. Existing approaches show limited capacity to capture and represent the evolving and nuanced nature of real-world behavioral dynamics, which are essential for informing personalized behavior change interventions.

Social media is increasingly used for research recruitment and knowledge mobilization. However, the general use of social media has not been documented among individuals living with chronic pain.

Differentiating among liver disease entities such as autoimmune liver disease (AILD), drug-induced liver injury (DILI), and chronic hepatitis B (CHB) remains clinically challenging due to overlapping clinical manifestations and nonspecific laboratory findings. Conventional machine learning (ML) approaches rely mainly on structured laboratory data, whereas free-text clinical reports and other heterogeneous electronic medical record data are often underused. Large language models (LLMs) may provide a strategy for encoding heterogeneous clinical information, yet their usefulness for liver disease classification remains insufficiently evaluated.

Early and accurate diagnosis of systemic lupus erythematosus (SLE) and its organ involvement is essential. Previous reviews of machine learning (ML) in SLE combined heterogeneous tasks and validation strategies and may have overinterpreted model performance.

Large language models (LLMs) are increasingly demonstrating the potential to reach human-level performance in generating clinical summaries from patient-clinician conversations. LLMs are usually evaluated against clinical summaries that focus mainly on patients’ biology and not on their biography (eg, preferences, values, wishes, and concerns). To achieve patient-centered care, artificial intelligence clinical summarization must incorporate patient-centered domains, implemented through patient-centered summaries (PCSs).

Ecological momentary interventions (EMIs) offer a promising strategy for targeting putative mechanisms of mental health problems by delivering real-time, tailored intervention components that adapt to person, moment, and context based on data collected using ecological momentary assessment (EMA). However, most research to date focuses on effects on distal outcomes, at the person level, whereas exploration of processes at the microlevel, that is, proximal effects of EMI components on putative momentary mechanisms and outcomes, remains very limited.

Digital health has provided caregivers with access to supportive resources without space-time restrictions. Caregivers’ digital health engagement behaviors can help them track their own health and that of care recipients as well as communicate with others. While digital health tools have become more prevalent since the COVID-19 pandemic, the trend in caregiver engagement has been less explored.

A substantial proportion of clinically relevant information remains locked in unstructured narrative documents, creating a bottleneck for clinical research, biobank annotation, registry development, and real-world evidence generation. While large language models (LLMs) enable advanced clinical text mining, adoption is constrained by concerns regarding data security, multilingual performance, and reproducibility. Manual data abstraction remains predominant for registry curation and retrospective research, despite being labor intensive, costly, and prone to variability.