JMIR Formative Research

Tobacco use remains the leading cause of preventable mortality in the United States; yet, evidence-based cessation services remain underused due to staffing constraints, limited access to counseling, and competing clinical priorities. Generative artificial intelligence (GenAI) chatbots may address these barriers by delivering personalized, guideline-aligned counseling through naturalistic dialogue. However, little is known about how GenAI chatbots support smoking cessation at both outcome and communication process levels.

For decades, the measurement of sleep and wake has relied upon watch-based Actigraphy as an alternative to expensive, obtrusive, clinical monitoring. To date, we have relied upon a handful of algorithms to score actigraphy data as sleep or wake. However, these algorithms have largely been tested and validated with only small samples of young healthy individuals.

Early identification of the etiology of spontaneous intracerebral hemorrhage (ICH) could significantly contribute to planning a suitable treatment strategy. A notable radiomics-based artificial intelligence (AI) model for classifying causes of spontaneous ICH from brain computed tomography (CT) scans has been previously proposed.

The application of large language models (LLMs) in medicine is rapidly advancing. However, evaluating LLM capabilities in specialized domains such as traditional Chinese medicine (TCM), which possesses a unique theoretical system and cognitive framework, remains a sizable challenge.

This pilot study offers preliminary evidence that a virtual meal-preparation task is feasible for older adults and highlights that the community engagement studios are an effective approach to generate community-informed strategies to enhance intervention designs and reach.

Early cancer detection is crucial, but recognising the significance of associated symptoms such as unintended weight loss in primary care remains challenging. Clinical Decision Support Systems (CDSS) can aid cancer detection, but face implementation barriers and low uptake in real-world settings. To address these issues, simulation environments offer a controlled setting to study CDSS usage and improve their design for better adoption in clinical practice.

While digital health solutions are becoming increasingly sophisticated, simple forms of everyday digital support may offer underexplored opportunities to promote health among older adults. However, evidence remains scarce on whether such teleassistance approaches can effectively enhance health literacy and daily self-care, particularly among populations facing socioeconomic and educational disparities.

The “Archive of German language general practice” (ADAM) stores about 500 paper based doctoral theses from 1965 till today. While they have been grouped in different categories no deeper systematic process of information extraction (IE) has been performed yet. Recently developed Large Language Models (LLMs) like ChatGPT have been attributed the potential to help in IE of medical documents. However, there are concerns about hallucination of LLM. Furthermore, there have not been reports regarding their usage in non-recent doctoral theses yet.

Google Street View (GSV) images offer a unique and scalable alternative to in-person audits for examining neighborhood built environment characteristics. Additionally, most prior neighborhood studies have relied on cross-sectional designs.

The rapid expansion of mobile health (mHealth) apps has transformed health care delivery worldwide. Despite their potential to improve epilepsy care, a substantial treatment gap remains, especially in low- and middle-income countries, due to limited resources, stigma, and low adoption of digital technologies. Although mHealth apps can bridge these disparities, their impact depends on acceptance and use by the target population.

Robotic-assisted surgery (RAS) has grown rapidly in recent decades, and several RAS procedures have become the standard. However, the physical and mental demands of minimally invasive surgery (MIS) techniques can lead to ergonomic shortcomings for surgeons. Advances in wearable technology and artificial intelligence favor the development of innovative solutions to analyze and improve ergonomic conditions during surgical practice.

Research concerning the long-term health consequences of induced abortion is constrained by both the limitations in the availability of data necessary to construct complete reproductive histories, as well as the limitations in the analytical methods necessary to interpret them.