Journal of Medical Internet Research

Large language models (LLMs) show great potential for widespread application in the field of nursing; however, their limitations include hallucinations, insufficient domain knowledge, and delays in updating evidence. Retrieval-augmented generation (RAG), by combining external knowledge retrieval with text generation, holds promise for optimizing model outputs. However, current evidence on the use of RAG in nursing remains fragmented and has not been systematically synthesized.

Online peer-support communities have become an increasingly important source of mental health information and support, particularly for individuals experiencing depression and anxiety. Platforms such as Reddit provide anonymous, accessible spaces where users can seek advice, share experiences, and receive community feedback. While these communities may help reduce barriers associated with stigma and access to care, concerns remain regarding the reliability, safety, and clinical appropriateness of peer-generated advice. Despite their widespread use, little empirical evidence exists regarding the extent to which highly endorsed online advice reflects evidence-based clinical recommendations.

Postoperative mortality remains a key indicator of surgical safety and quality. Traditional risk models based on static preoperative or intraoperative variables often fail to capture dynamic physiological changes during surgery and generalize poorly across surgical types. To address these limitations, we developed and validated a deep learning framework capable of learning heterogeneous temporal patterns from intraoperative signals while maintaining interpretability and adaptability.

Pediatric care resources are limited and unevenly distributed in many health systems. Caregivers increasingly turn to large language models (LLMs) for children’s health information, but real-world evidence on LLM use and response quality in general pediatric care is limited.

Digital decision-support tools, such as the Stroke Triage App (STA), may enhance prehospital triage and clinical decision-making for suspected patients with stroke. However, implementation of decision-support tools and their impact on clinical practice remains insufficiently studied.

Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) represent critical windows for early neurocognitive intervention, although access to timely, personalized rehabilitation remains limited. Digital health technologies offer opportunities to deliver scalable and individualized cognitive care; however, their successful implementation depends on an understanding of users’ needs, expectations, and barriers. Within a human-centered design (HCD) framework, actively involving end users during the earliest stages of development is essential to ensure that digital rehabilitation is acceptable, usable, and responsive to real-world rehabilitation needs.

Head and neck squamous cell carcinoma (HNSCC) represents a significant public health challenge. Its treatment is characterized by high anatomical complexity and a critical need for functional preservation of speech, swallowing, and respiration. While multimodality care, including surgery, radiotherapy, and systemic therapy, has substantially improved patient survival, there are still high rates of life-altering toxicities and low response rates to immunotherapy. Current clinical decision-making largely relies on “digital snapshots”—static representations derived from population-based statistics and time point–specific imaging—which usually fail to account for the rapid anatomical and biological change during the course of treatment. Digital twins hold great promise in accelerating scientific breakthroughs and transforming oncology treatment and precision medicine through the creation of high-fidelity virtual patient representations that integrate real-time biological, anatomical, and clinical data. In this viewpoint, we propose 3 interconnected digital twins to establish a conceptual framework for the HNSCC digital twin. These digital twins include the anatomical twin, using virtual surgical planning and augmented reality for geometric precision; the dosimetric twin, using daily imaging and synthetic computed tomography generation for automated adaptive radiotherapy; and the biological twin, integrating deep phenotyping, radiomics, and mechanistic omics to simulate individualized therapeutic trajectories. Beyond acute care, we explore the clinical utility of digital twins in navigating the “de-escalation dilemma” for human papillomavirus–associated disease and improving survivorship through digital phenotyping. With the advent of longitudinal data from medical-grade Internet-of-Things sensors and circulating tumor DNA kinetics, digital twins can serve as active sentinels, detecting functional decline or subclinical events months before clinical manifestation. Furthermore, the implementation of synthetic control arms via virtual populations offers an avenue to accelerate clinical trials and address ethical issues in rare molecular subtypes. Despite this potential, substantial barriers to implementation remain, including profound anatomical instability during treatment, the “black box” nature of deep learning algorithms, and the challenges of multiscale data integration. We provide a technical roadmap for the development of “morphing” digital twins—dynamic systems capable of continuous geometric and biological auditing. By bridging the gap between the macroscopic world of radiation physics and the microscopic world of cellular evolution, digital twins may help shift the management of HNSCC from population-based statistical probability toward more individualized, biologically informed decision-making, with the goal of optimizing HNSCC management while preserving essential patient functions.

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), including semaglutide and tirzepatide, are highly effective for weight management; however, long-term outcomes depend on sustained behavioral engagement. Digital health tools may enhance these behavioral components, but evidence regarding their real-world benefit alongside pharmacotherapy remains limited.

In Vietnam, antibiotic use without involvement of health care professionals is common in the community, despite the legal requirement for a prescription. In low- and middle-income countries, mobile health (mHealth) is perceived as having the potential to improve community health care at low cost.

People living with long-term physical health conditions who are also experiencing subthreshold depression are at risk of developing major depression. In partnership with lived experience experts, clinicians, and software developers, we co-designed CommonGround, an online peer support and psychoeducation platform. CommonGround’s Theory of Change (ToC) was also conceptualized to outline the intermediate outcomes (and associated indicators of whether such outcomes are achieved) needed to improve the mental health of the target population.

Illicit substance use disorders (SUDs) are escalating globally; yet, social stigma and legal barriers restrict access to conventional treatment, and existing research remains skewed toward alcohol and nicotine. Digital interventions incorporating cognitive behavioral therapy (CBT) offer a discreet, scalable alternative.

Social media and search engine tech companies, such as Google, use the wealth of data at their disposal to create algorithms to predict suicide risk and provide interventions based on company policies and outputs from these algorithms. Neither the details of these algorithms nor their accuracy in risk prediction are shared with the public; thus, research is needed to verify whether algorithms applied to search engine data can accurately predict suicide risk in a platform user. This paper provides new information about the methodological challenges in studying the value of search engine data for suicide prediction.