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Skip search results from other journals and go to results- 9097 Journal of Medical Internet Research
- 4361 JMIR Research Protocols
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This study does not involve human subjects, identifiable private information, or direct interactions with individuals. Instead, it relies exclusively on publicly available, anonymized social media posts. Consequently, institutional review board approval was deemed unnecessary.
The LDA analysis identified 310 topics. In thematic analysis, the team considered the topics identified, groups of words, and representative blog post samples in each topic and categorized the 310 topics into 58 subthemes.
JMIR AI 2025;4:e64447
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An option to enter their email address to be contacted for the interview; a separate interview information sheet and consent form were then emailed to participants.
The survey was designed to obtain demographic data and staff’s experience with and perception of DMHIs. The questions are tailored by the research team to the study objectives and focus on the following categories:
Demographic information, including gender, age, job role, and time being an HCP.
JMIR Ment Health 2025;12:e67190
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We conducted both clinician and patient interviews until reaching thematic saturation on two criteria, (1) no new concepts were identified in iterative analysis interviews (code frequency counts) and (2) there was consistent repetition among interviewee responses without any new information being added to existing codes (code meaning) [19,20].
JMIR Cardio 2025;9:e66215
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Mobile Apps and Wearable Devices for Cardiovascular Health: Narrative Review
While simply providing an app or wearable in German did not automatically qualify it as DACH-specific, those developed by creators from Germany, Austria, or Switzerland, with a focus on their respective health care systems, were deemed as such. Secondly, the app or wearable was considered DACH-specific if the company behind it was based in one of the DACH countries.
JMIR Mhealth Uhealth 2025;13:e65782
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For example, an AI might generate content that includes incorrect information, such as asserting that “the human heart only has two chambers” [24], or misinterpret the complexity level required for a medical education context. Furthermore, the “black box” [25] nature of these AI models complicates diagnosing and correcting these errors within the AI mechanism, as it is challenging to trace back how the AI arrived at a particular output.
JMIR Form Res 2025;9:e65726
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These maternal background and perinatal data were obtained by combining information from medical records and parents’ reports up to the first month postpartum check-up. The median maternal age was 36 (IQR 32-29) years, and 3 (N=144, 2%) mothers had a smoking history during their current pregnancy. The most prevalent educational level for both parents was a university degree (48/144, 33% mothers and 64/144, 44% fathers).
JMIR Pediatr Parent 2025;8:e58337
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However, existing research shows varied effects of VR training systems on upper extremity function, with differences particularly evident between commercial and customized VR systems. Commercial VR systems, designed primarily for general consumer use, are often marketed for gaming and entertainment purposes rather than rehabilitation [17].
J Med Internet Res 2025;27:e69003
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Modernizing the Staging of Parkinson Disease Using Digital Health Technology
Given this difficulty, every effort must be made to improve diagnostic accuracy in PD [6] to provide individuals with PD with more accurate prognostic information, facilitate enrollment in early disease-modifying trials in PD, and initiate therapeutics to improve quality of life.
J Med Internet Res 2025;27:e63105
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The Role of AI in Nursing Education and Practice: Umbrella Review
The studies predominantly focused on 3 domains: research, which emphasized the development, validation, and ethical considerations of AI technologies; practice, which explored AI’s integration into workflows, decision-making, and care delivery, including predictive analytics and clinical decision support systems; and education, which examined AI’s potential in transforming nursing training through tools such as large language models (LLMs), virtual reality (VR) simulations, and personalized learning systems
J Med Internet Res 2025;27:e69881
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CDSSs are valuable tools that assist clinicians in their decision-making processes by providing targeted clinical knowledge, patient information, and other relevant health data. CDSSs play a central role in managing large amounts of data [29] and ensuring the retrieval of relevant information. CDSSs do not make decisions autonomously. However, the integration of machine learning and AI, such as CNNs, has significantly enhanced their capabilities [30].
J Med Internet Res 2025;27:e58660
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