e.g. mhealth
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Skip search results from other journals and go to results- 4642 Journal of Medical Internet Research
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The publisher expresses concern regarding the following article:
A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study [1].
This article is under investigation for potential peer review irregularities. Readers are advised to interpret the findings with caution pending the outcome of this inquiry.
JMIR Med Inform 2025;13:e75352
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It serves as a dynamic tool that stresses the importance of learning from intercoding differences rather than seeking strict agreement and validation, as is valued among qualitative researchers [37].
Agreement between large language model (LLM) and human coding.
We then use GPT-4 for topic comparison, accessing the Chat GPT engine through an application programming interface (API) for programmatic purpose.
JMIR AI 2025;4:e64447
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Being immersed in a highly digitized workplace, participants in both countries reflected on the additional effort and learning curve entailed by systems like the electronic health record. They demonstrated hesitance and reluctance to engage with more digital systems like DMHIs, with a strong need for a “screen break” (as shown in the survey feedback and Subtheme 2.2).
JMIR Ment Health 2025;12:e67190
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I think it would be alright as long as I know they’re checking my machine and make sure it’s up running.
Some patients mentioned that this new model of care would reduce the burden on their VHA clinic, and help other veteran patients get care.
Your clinician can actually be seeing somebody that’s really in need instead of doing a basic maintenance check.
Of 22 clinicians interviewed, 20 (87%) participated in the survey, 14 (64%) of which were fully complete.
JMIR Cardio 2025;9:e66215
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Unlike the template-based method, this method uses the ability of artificial intelligence (AI) to generate content dynamically, for example, using Chat GPT, which is an AI-based chatbot developed by Open AI, for creating items based on specific topics or learning outcomes provided by users [17-21]. This approach allows for the generation of diverse and complex questions in seconds, offering flexibility and efficiency in item development.
JMIR Form Res 2025;9:e65726
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Reference 39: Autism, attachment, and social learning: three challenges and a way forwardMachine Learning
JMIR Pediatr Parent 2025;8:e58337
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In home-based settings, routine care often lacks sufficient intensity and task-specific training, which are critical for motor learning and functional recovery [3]. Similarly, traditional therapies at home may suffer from limited professional supervision, reduced patient motivation, and lower adherence rates compared to hospital-based programs [5,6].
J Med Internet Res 2025;27:e69003
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Modernizing the Staging of Parkinson Disease Using Digital Health Technology
technology (eg, smartphones, tablets, and wearable devices) and the substantial increase in its use in clinical settings (ie, with a more than 5-fold increase over the past 5 years and an expected 10-fold growth in the next 3 to 5 years [7]) provides a prudent opportunity to address this problem by implementing (1) innovative sensing modalities (eg, device sensors and human-device interactions [8]), (2) accurate PD detection through advanced computer-assisted techniques (eg, artificial intelligence [AI] [9] and machine
J Med Internet Res 2025;27:e63105
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The Role of AI in Nursing Education and Practice: Umbrella Review
AI encompasses a broad spectrum of technologies that enable machines to mimic human intelligence, including machine learning, natural language processing, robotics, and computer vision [14,15]. In health care, AI has been leveraged for tasks such as disease diagnosis, treatment planning, patient monitoring, and administrative operations [16,17].
Globally, substantial investments in AI technologies reflect a recognition of their potential to improve health care delivery.
J Med Internet Res 2025;27:e69881
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However, the integration of machine learning and AI, such as CNNs, has significantly enhanced their capabilities [30].
Among various AI systems, CNNs have been extensively used for automated image recognition, particularly in the diagnosis of skin cancer and melanoma [31]. Extensive research has shown that the accuracy of CNNs is comparable to that of dermatologists [32-34].
J Med Internet Res 2025;27:e58660
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