JMIR (Journal of Medical Internet Research) in the AI ethics record
A source-linked view of 132 research records gathered from JMIR (Journal of Medical Internet Research). This page tracks what entered the ethics.ai source fleet; it is not a complete archive of the publisher and does not imply its endorsement.
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Latest records from JMIR (Journal of Medical Internet Research)
Background: Patient-facing digital health tools such as mobile health apps, wearables, and digital therapeutics have expanded rapidly and show promise for improving chronic disease management. Despite increasing evidence of effectiveness, health systems and payers continue to face challenges integrating these tools into routine care. Objective: This study examined the decision-making processes of health system and payer leaders regarding the adoption and sustainability of patient-facing digital
Background: Early risk stratification in emergency medical services (EMS) is essential for patients presenting with acute cardiopulmonary symptoms, yet prehospital decision-making at the dispatch stage is often based on limited structured information. Free-text dispatch narratives may contain additional clinical signals, but their role in early risk assessment remains insufficiently characterized. Objective: This study aims to develop and temporally validate a natural language processing–assiste
Background: The exponential expansion of biomedical literature has created an urgent need for efficient methods to recognize and extract population, intervention, comparison, and outcome (PICO) elements—the foundational elements of evidence-based medicine. Objective: This study systematically evaluated 2 complementary approaches for automating PICO recognition and extraction in medical literature: prompt engineering optimization and parameter-efficient fine-tuning (PEFT) of large language models
Background: Although mobile health (mHealth) interventions serve as potential solutions for addressing mental health problems, evidence on whether mHealth interventions targeting physical activity can reduce psychological distress among generally healthy workers is limited. Objective: This study aimed to investigate the effectiveness of a stand-alone smartphone app, which passively monitors physical activity and psychological distress, in reducing psychological distress among workers. Methods: T
Background: Non–small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality worldwide. Accurate early prediction of response to neoadjuvant therapy is critical. Objective: We aimed to evaluate the diagnostic performance of radiomics-based AI in predicting pathological complete response (pCR) and major pathological response (MPR) following neoadjuvant immunochemotherapy in NSCLC and to compare it against traditional radiological criteria. Methods: A systematic search of P
Advances in digital health have dramatically changed how patients engage with their health. Rather than relying solely on periodic clinical visits, patients now have access to smartphones, patient portals, wearable devices, and mobile apps that provide support for day-to-day self-care decisions. This commentary discusses the findings of Longhini et al’s systematic review and meta-analysis on the effectiveness of digital health interventions, which found modest improvements in self-care monitorin
Background: Chronic heart failure (CHF) significantly impairs physical function and quality of life. Although exercise-based cardiac rehabilitation represents a primary therapeutic strategy, participation rates remain low due to logistical barriers. Digital health technologies (DHTs) offer a promising alternative to deliver home-based interventions. However, evidence regarding their specific impact on functional capacity versus daily physical behavior remains inconsistent. Objective: This system
Background: AI has the potential to transform health care in low- and middle-income countries, where access to quality care remains limited. Maternal, sexual, and reproductive health (MSRH) outcomes are especially poor due to resource shortages, financial barriers, and geographic inequities. With thoughtful implementation, AI could help address these gaps through innovations in diagnostics, health education chatbots, and telemedicine. However, responsible use is essential to ensure AI reduces, r
In this retrospective cohort of 5132 users of a commercial nutrition-tracking mobile application, higher food-tracking frequency was associated with greater weight loss over 6 months; 70.1% (3599/5132) of users lost at least 5% of body weight.
Background: Online social support, the interaction among individuals in which one helps another during difficult situations through online platforms such as online forums or social media, has proliferated as a vital tool for personal mental health care. Despite the growing usage and importance of online social support, prior studies have mainly focused on either understanding the characteristics of support seekers or merely identifying types of support, which leaves room for improvement in 2 key
Background: Internet addiction (IA) has been consistently associated with adverse mental health outcomes, but less is known about whether adolescents with IA seek mental health support, and whether associations between help-seeking and mental health problems differ across pathways. Objective: This study aimed to describe mental health help-seeking patterns across internet use and IA status, and examine the independent and interactive associations of IA and help-seeking with mental health problem
Background: Online reviews of health care services represent a growing source of unsolicited, citizen-generated data that can complement traditional instruments for monitoring public perception of health systems. However, longitudinal analyses examining how citizens’ perceptions evolved before, during, and after the COVID-19 pandemic remain scarce, and existing studies have rarely differentiated between levels of care. Objective: This study aimed to examine the longitudinal evolution of public p
Background: The current global status of breastfeeding is marked by both progress and challenges. Digital health interventions (DHIs) have emerged as a promising strategy for improving breastfeeding practices, yet evidence regarding their impact on breastfeeding outcomes remains limited. Objective: This study aimed to evaluate the impact of DHIs on breastfeeding practices and outcomes. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we s
Background: The emergency intensive care unit (EICU) manages the most critically ill patients, where rapid and accurate diagnosis is essential yet challenging. Diagnostic error rates in this setting are more than twice as high as in general wards, with serious consequences for patient outcomes. Large language models (LLMs) have attracted growing interest as decision-support tools; however, direct comparative evidence between critical care–specialized and general-purpose LLMs across the admission
Background: Influencer marketing (paid promotion by individuals with large, engaged social media followings) has become a major commercial advertising strategy, projected to reach US $32 billion globally in 2025. Clinical trials increasingly recruit through digital channels such as social media advertisements and patient portal messages. However, to our knowledge, influencer marketing has not been described as a clinical trial recruitment modality, and no practical guidance exists for investigat
A digitally enabled health system offers the opportunity to address gaps in the implementation of shared decision-making, a collaborative process between health professionals and consumers to decide on the best test, treatment, or management option based on clinical evidence and the consumer’s values and informed preferences. There is increasing design and availability of digital tools online to support shared decision-making. Providing opportunities for all consumers to make shared health care
Background: The use of social media in cancer research, patient support, and information sharing has been well documented. Objective: Using retinoblastoma as a model, we use the information provided from Twitter (subsequently rebranded X) to understand patients’ treatment-seeking behavior and barriers, as well as investigate its application in research and epidemiology for rare diseases. Methods: Posts on retinoblastoma were extracted from Twitter. We trained BERT (Bidirectional Encoder Represen
Background: Chronic medical illnesses coexist with mental health challenges, negatively impacting quality of life and well-being. Compassion-based interventions have shown promise for individuals with chronic conditions, yet accessibility barriers limit their implementation. Internet-delivered formats may address these limitations while maintaining effectiveness. To our knowledge, no fully self-guided, internet-delivered attachment-based compassion intervention has been tested in a transdiagnost
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