e.g. mhealth
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Skip search results from other journals and go to results- 51 Journal of Medical Internet Research
- 45 JMIR Formative Research
- 28 JMIR Research Protocols
- 20 JMIR Medical Education
- 15 JMIR mHealth and uHealth
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For instance, the High-Level Expert Group on AI has established the Assessment List for Trustworthy Artificial Intelligence to self-assess the trustworthiness of AI systems. This framework is based on 7 key requirements, namely human agency and oversight, technical robustness and security, privacy and data governance, transparency, diversity and fairness, societal and environmental well-being, and accountability [13].
J Med Internet Res 2025;27:e73710
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The PI then presented the results of the needs assessment to the Cardiothoracic Fellowship Program Director and the Residency Program Director at our institution. The results of the needs assessment are summarized in Textbox 1.
The results of our needs assessment are summarized as follows:
At the time of writing, there were 4 institutions that deliver formal cardiac implantable electronic device (CIED) didactics to cardiothoracic anesthesia trainees and fellows.
JMIR Med Educ 2025;11:e60087
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Natural Language Processing for Identification of Hospitalized People Who Use Drugs: Cohort Study
assessment
JMIR AI 2025;4:e63147
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The Pain Pad device has several advantages over other pain assessment methods in a hospital setting. Paper-based or verbal pain assessments can be limited by patient fatigue, cognitive impairment, and verbal communication difficulties and often require manual transcription that could lead to errors [32,33].
JMIR Hum Factors 2025;12:e65271
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A quality assessment of the included articles will be conducted using the tool developed by Kmet and colleagues [16]. While this step is not mandatory in a scoping review, its inclusion will enhance the analysis. This tool provides standardized criteria for evaluating the methodological quality of primary research papers across various fields.
JMIR Res Protoc 2025;14:e74225
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The Rapid Online Cognitive Assessment for the Detection of Neurocognitive Disorder: Open-Label Study
The Rapid Online Cognitive Assessment (Ro CA) is a DCA that aims to address these limitations. To stay consistent with well-validated methods, it reproduces the screening results of gold-standard paper-based tests: Addenbrooke’s Cognitive Examination-3 (ACE-3) and the Montreal Cognitive Assessment (Mo CA) [28-31]. Patient input directly guided its design to ensure accessibility, resulting in a short touchscreen-based drawing battery with automated convolutional neural network–based scoring [32,33].
J Med Internet Res 2025;27:e66735
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This methodology [16] was adapted from a comparative analysis of assessment frameworks for sustainability [17]. The analysis addressed qualitatively and descriptively the (1) rationale for assessment, (2) assessment framework characteristics, (3) assessment framework governance, (4) certification workflows and techniques used, (5) quality aspects and subaspects of each framework, and (6) quality requirements (or criteria).
JMIR Mhealth Uhealth 2025;13:e67858
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Prior to the full analysis, both reviewers conducted a pilot assessment on a subset of 5 websites to refine the coding framework and confirm a shared understanding of the evaluation criteria. Any discrepancies during the content analysis were resolved through consensus discussion, with additional input from a health care professional when necessary. This process ensured interrater agreement and strengthened the reliability of the evaluation.
JMIR Form Res 2025;9:e64630
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It was found to be able to quickly and accurately apply known concepts in medicine to novel problems, including reflection prompts and examination questions, and to mimic human writing styles, introducing a potential threat to the validity of traditional forms of medical student assessment including short answer assessment [8], it even successfully passed the USMLE (United States Medical Licensing Examination) [9].
JMIR Med Educ 2025;11:e69521
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Recent advancements in large language models (LLMs) have opened up exciting possibilities for quantitative assessment of neurological diseases [7,8]. Due to their transformer architecture, LLMs project strings of text (sentences, paragraphs, etc) onto a high-dimensional embedding space that represents the semantic and syntactic relationships between words and phrases. In this embedding space, linguistically similar texts are geometrically co-located.
JMIR AI 2025;4:e67369
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