JMIR Formative Research

Advances in hardware and software have transformed how users perform activities across various domains by integrating technology in ways that enhance task execution. In education, these processes facilitate pedagogical innovation and learning through diverse interaction models, such as web systems, augmented or virtual reality, and mobile devices. An example of these approaches occurs in the context of cytopathology. Digitization and slide visualization technologies allow the use of real images in a computational environment, enabling an alternative model of interaction between the professional and the samples.

Young and middle-aged patients with hypertension often show poor self-management due to limited cognitive bandwidth. Mobile health apps using behavioral economics may be helpful for these patients.

Medical students must build skills beyond traditional clinical domains to best shape the evolving health care system and fill a variety of professional roles after graduation. AI offers new methods for medical education.

Recent work has demonstrated that individuals with degenerative cervical myelopathy (DCM) often experience symptoms of fatigue that are underrepresented in the literature. Conversely, understanding fatigue is highly important to people living with DCM. The AO Spine RECODE-DCM (Research Objectives and Common Data Elements for Degenerative Cervical Myelopathy) study group recently determined that no suitable fatigue scales exist for DCM.

Though attention deficit hyperactivity disorder (ADHD) is thought to be the most prevalent neurodevelopmental disorder in young people worldwide, there are inequalities in access to psychoeducation and health care support. One way to improve access, potentially increase engagement, reduce health care inequalities, and enhance care is by co-developing digital responsive interventions. These have the potential to support long-term condition management and to act as an adjunct to usual care. Virtual assistants that use large language models can provide information in response to questions and learn to tailor communication to suit an individual user’s needs. This can be especially valuable for people with ADHD who often struggle to regulate attention and can experience communication challenges. Involving people with lived experience in the co-design process is crucial for the development of effective digital interventions. Therefore, this article explores the views and preferences of young people with ADHD and their supporters from the United Kingdom who collaborated with researchers to co-design a prototype chatbot.

Alzheimer disease and age-related cognitive decline reduce memory engagement and limit caregiver insight, creating a need for accessible tools that support everyday cognitive activity in older adults. Although AI holds promise for personalized cognitive support, few AI-based apps have been developed and evaluated for memory engagement in this population, and fewer incorporate on-device emotional analysis with privacy-preserving design.

Targeting automatic approach tendencies toward alcohol-related stimuli has been shown to significantly reduce relapse rates in patients with alcohol use disorder (AUD) when used as an add-on to inpatient treatment. Traditional approach-avoidance task (AAT) training requires a clinical setting and a stationary device.

Chronic kidney disease (CKD) affects over 850 million individuals worldwide and requires sustained patient education, shared decision-making, and self-management support. Digital health platforms may facilitate these needs; however, many lack comprehensive, user-centered design and fail to address practical and psychosocial patient concerns.

AI technologies refer to computer-based systems designed to perform tasks that typically require human intelligence and have been increasingly used in obstetrics and gynecology (O&G).

The COVID-19 pandemic highlighted the importance of timely infectious disease surveillance. In Japan, conventional sentinel and claims-based systems incur reporting lags and capture limited clinical detail, whereas free-text clinical notes in electronic health records (EHRs) hold richer, timelier symptom and vaccination information. Natural language processing (NLP) with large language models (LLMs) offers a way to structure such free text at scale.

Large language models (LLMs) are increasingly used to generate differential diagnoses from clinical narratives. However, LLM-based diagnostic clinical decision support systems still lack a quantitative measure of how strongly a diagnosis is supported by the available case description. Conditional perplexity score quantifies how predictable a target text is given in a preceding context, with lower scores indicating greater predictability. We hypothesized that this concept can be adapted to diagnostic reasoning by treating the prediagnostic case description as the context and a diagnosis as the target text.

Long-term remote patient monitoring of weight, pulse, and blood pressure has been shown to significantly reduce mortality and hospitalization rates among patients with heart failure. Despite its proven effectiveness, maintaining patient engagement in remote monitoring programs remains challenging.