e.g. mhealth
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Skip search results from other journals and go to results- 156 Journal of Medical Internet Research
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DWCSs integrate artificial intelligence (AI) to monitor wound progress and identify potential risks [23], and they are interoperable with organizational systems, allowing efficient and secure data exchange. The seamless data exchange through AI technologies [24] is crucial for establishing a cohesive wound care program that can adopt and scale up digital documentation and objective AI assessment data.
JMIR Nursing 2025;8:e71535
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Due to the high level of uncertainty and criticality in health care and problem diversity, our objective was to introduce humanlike cognitive capabilities into artificial intelligence (AI) systems to develop an augmented intelligence approach. While AI, ML and other automation technologies have make substantial advances in recent years, many important health care problems are often solved through the collaboration of human beings and machines [24-27].
J Med Internet Res 2025;27:e56080
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Artificial intelligence (AI) chatbots have emerged as innovative tools for delivering personalized, leveraging advanced capabilities to address barriers in health communication [16]. Unlike static interventions, such as printed materials or one-size-fits-all campaigns, AI chatbots offer interactive, real-time engagement, making them particularly effective in addressing nuanced health behaviors and misconceptions [16].
Asian Pac Isl Nurs J 2025;9:e71865
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While we have adopted our working definition of a DMHI to facilitate a systematic search strategy, this definition may evolve as the use and development of DMHIs expands and the potential role of artificial intelligence (AI) in digital mental health crystallizes. We did not limit the methodologies of the included studies, and therefore, our review provides a broad overview of the subject area as opposed to a focused exploration of either qualitative or quantitative research.
J Med Internet Res 2025;27:e68544
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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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Modernizing the Staging of Parkinson Disease Using Digital Health Technology
Specifically, supervised ML and AI-enabled detection of health data has shown potential in the area of disease prediction, classification, and monitoring of overall progression [36-39].
J Med Internet Res 2025;27:e63105
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Research indicates that users often struggle to find the right balance, either overtrusting and overly relying on AI recommendations or undertrusting and disregarding helpful advice [8,9]. A balance of trust is needed to appropriately rely on these systems and achieve beneficial human-AI collaboration.
J Med Internet Res 2025;27:e58660
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The study results were reported in accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis plus Artificial Intelligence (TRIPOD+AI) statement [45].
Baseline characteristics of participants in this study are shown in Table 1. The participants in the internal validation dataset had a mean age of 71.0 years. Most of the participants were male and had finished primary school.
JMIR Aging 2025;8:e62942
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Authors’ Reply: Citation Accuracy Challenges Posed by Large Language Models
Collaborative efforts among academic institutions, publishers, and AI developers are essential to establish standardized protocols and reliable training datasets. Such partnerships would not only enhance the reliability of LLM-generated outputs but also foster greater trust in AI-assisted scholarly communication.
Moreover, the broader academic community bears responsibility for critically appraising AI-generated content.
JMIR Med Educ 2025;11:e73698
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Citation Accuracy Challenges Posed by Large Language Models
ai AI-based toolsArtificial Intelligence (AI) in Medical Education
JMIR Med Educ 2025;11:e72998
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