Journal of Medical Internet Research

Large language models (LLMs) are increasingly applied in healthcare. However, concerns remain that their nursing care recommendations may reflect patients’ sociodemographic attributes rather than clinical needs. While this risk is acknowledged, there is a lack of empirical evidence evaluating sociodemographic bias in LLM-generated nursing care plans.

Digital mental health interventions (DMHIs) offer a scalable approach to address adolescent depression and anxiety. User-centred co-production can optimize acceptability and engagement, but it is often resource-intensive. Advances in generative artificial intelligence (AI) create new opportunities for involving adolescents in co-design, yet research on its feasibility and acceptability, particularly in low-resource settings, remain underexplored.

DeepSeek is an open-source large language model (LLM) and significantly accelerates LLMs adoption in healthcare. Its rapid deployment has sparked concerns of its impact on patient outcomes and safety. However, little is known about how DeepSeek is used and regulated in these facilities.

Artificial intelligence (AI) holds great promise in transforming healthcare delivery. However, successful implementation of AI projects in healthcare depends on patients' acceptance and trust. There is only limited empirical research examining public perceptions, particularly on the use of personal health data in AI applications in healthcare.

The prevalence of inflammatory bowel disease (IBD) has been on the rise, with adolescents and young adults experiencing the highest incidence rates. For these young patients, self-management behaviors are critical to maintaining disease remission and improving quality of life and yet their current self-management status remains suboptimal.

Accurate assessment of voiding patterns before and after surgery for lower urinary tract symptoms is critical in patient care, but it places heavy burdens on both the patient and the clinic. While methods for telemedicine have been devised, no technology for acoustic assessment of urinary patterns has been prospectively evaluated for clinical use.

India faces a dual burden of diabetes and prediabetes. Although mobile health (mHealth) interventions have shown promise in promoting healthy lifestyle changes, most interventions deploy generic, “one-size-fits-all” messages that do not consider individual behavioral patterns, motivational states, or changing needs over time.

NHS Wales routinely collects patient-reported outcome measures, and these, together with other clinical data, offer an opportunity to design machine learning (ML) technologies that could advance the implementation of prudent health care principles (a health care strategy encouraged by the Welsh Government). However, the wide adoption of such technologies is not only dependent on the development of technically well-performing ML algorithms but also on end-user barriers and facilitators.

Wearable fitness technologies, like the Oura Ring, provide physiological metrics, like sleep and heart rate data, to a growing user base of young adults. However, these technologies and connected mobile applications do not measure young adults’ alcohol use that contributes to these metrics. Personalized feedback on the impact of alcohol on sleep and heart rate may boost motivation to reduce drinking among young adults.

The global mental health crisis is becoming increasingly severe. Due to the shortage of mental health professionals, high treatment costs, and insufficient accessibility of services, there is an urgent need for scalable and low-cost intervention methods. In recent years, chatbots have shown potential for psychological interventions. The efficacy differences between LLM-based and rule-based chatbots have not been systematically evaluated, with few studies directly comparing the two, and existing meta-analyses have notable limitations: there is high heterogeneity in intervention design (e.g., dialogue structure, interaction frequency, and duration) across studies, and there is a lack of direct comparison of differentiated intervention effects on depressive and anxiety symptoms, making it difficult to integrate conclusions.

Cyberchondria is often associated with psychological distress, straining doctor-patient relationships, and financial burdens. Over the past few decades, increasing research has explored its associations with quality of life (QoL). However, existing reviews have not comprehensively synthesized or narratively analyzed these connections.