e.g. mhealth
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Skip search results from other journals and go to results- 177 Journal of Medical Internet Research
- 72 JMIR Medical Education
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- 5 JMIR mHealth and uHealth
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It is positioned to benefit from advances in an even wider array of disciplines, including bioinformatics, data science, machine learning, artificial intelligence (AI), natural language processing, large language models (LLMs), systems pharmacology, pharmacogenomics, pharmacometabolomics, and health informatics [2-7].
JMIR AI 2025;4:e65481
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The potential use of generative AI was discussed to streamline medical documentation and improve patient care.
JMIR Cancer 2025;11:e70827
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The rise of AI has shown great promise, particularly in the field of wound care. These technologies provide health care professionals with novel tools that contribute towards many improvements in treatment efficiency and efficacy, including early detection, risk factor analysis, prediction, diagnosis, intelligent treatment, outcome prediction, and prognostic evaluation [6]. In addition, AI-powered tools have been shown to empower patients to take control of their own health and well-being.
J Particip Med 2025;17:e69470
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Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study
In addition to that, the interviewees formulated expectations regarding AI, especially for streamlining the scheduling processes. AI is expected to handle a wide array of individual requirements and preferences, such as avoiding late shifts on specific days or adjusting work schedules based on partial employment percentages. The AI system should also accommodate experience levels and qualifications, manage fluctuating monthly and annual work hours, and efficiently address overtime.
JMIR Form Res 2025;9:e67747
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Enhancing Antidiabetic Drug Selection Using Transformers: Machine-Learning Model Development
All implementations were written in Python 3.11 (Python Software Foundation) and Py Torch 2.2 (Meta AI).
The model was trained using prescription records from endocrinologists at the University of Tokyo Hospital. As a result, the model generates outputs aligned with the treatment approaches of these specialists.
We analyzed the characteristics of patients in the dataset using means, SD, and frequency counts. We calculated 95% CI using the bootstrap method where applicable.
JMIR Med Inform 2025;13:e67748
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The findings were then synthesized to highlight trends, gaps, and potential areas for future research in the application of AI in patch testing.
JMIR Dermatol 2025;8:e67154
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However, implementing AI solutions such as machine learning for seizure detection and monitoring is not yet a standard component of DCPE. Integrating a new data-driven medical software for EEG seizure monitoring into the current DCPE requires a thorough understanding of the user’s needs and system requirements.
JMIR Hum Factors 2025;12:e59558
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Furthermore, it is clear that data collection and evaluations of AI, electronic health records, and other digital applications in health care have only been conducted to a limited extent.
If we examine the publications in the field of AI, it becomes evident that it can contribute in many different ways to overcoming problems in the health care sector.
J Med Internet Res 2025;27:e71795
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