e.g. mhealth
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In addition, the AI tools’ inability to access paywalled literature introduces a potential bias toward open-access sources, which could affect the comprehensiveness and balance of the AI-generated output.
Overall, expert readers were impressed by the results but noted a lack of scientific depth and nuance—the elements that distinguish strong review articles from mediocre ones.
J Med Internet Res 2025;27:e75666
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While there is no consensus on what AGI is, one may view an AGI system as a form of artificial intelligence (AI) with a general scope with the ability to perform well across various goals and contexts [17]. Finally, Yuan et al [82] provided a broad review of the applications and implications of LLMs in medicine, especially MLLMs, and discussed the emerging development of LLM-powered autonomous agents.
J Med Internet Res 2025;27:e71916
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Reference 6: Designing an AI health coach and studying its utility in promoting regular aerobic exerciseai
JMIR Form Res 2025;9:e73807
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Machine learning, as a subset of artificial intelligence (AI) techniques, analyzes large datasets to identify patterns and make predictions [154]. These methods are transformative in gender studies, uncovering patterns in gender-related data and deepening the understanding of social inequalities and biases [155]. These methods have been successfully applied to areas such as forecasting gender-based violence, further demonstrating their relevance in social sciences [156].
JMIR Res Protoc 2025;14:e66396
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The literature describes a further wide range of problems and barriers in the context of AI-based CDSS [10-12]. These relate to AI or CDSS and a combination of both, AI-based CDSS. While some of the problems relate to technical integration and operational use [10,11], others relate to the legal and ethical framework [12].
JMIR Med Inform 2025;13:e69688
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RL offers a more dynamic and adaptive solution, as it allows artificial intelligence (AI) models to learn optimal insulin dosing strategies through trial and error. Unlike traditional ML, RL does not require explicit supervision and can adjust insulin delivery based on real-time feedback from CGM data. Several RL-based frameworks have demonstrated improvements in personalized insulin dosing [4-7,11].
JMIR Diabetes 2025;10:e72874
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Participants typically felt unsure of exactly what AI is and how it differs from existing non-AI computer tools (15/20, 75%):
To me AI is almost something that is talked about and I don’t understand.
Participants typically had limited experience of the use of AI within the health care setting, and even those GPs who had experience working with AI technology companies did not feel that they fully understood AI.
J Med Internet Res 2025;27:e71980
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Modern CAC approaches often include AI since LLMs like Chat GPT have demonstrated impressive capabilities in natural language processing tasks. However, these generative AI models tend to perform poorly when applied to clinical coding [11,12]. This poor performance is perhaps largely explained by the vast and intricate label space of ICD codes (with thousands of specific options), and lack of localized, domain-specific, clinical data for training purposes.
J Med Internet Res 2025;27:e71904
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These mock-ups encompassed (1) an overview page featuring details on all study participants, (2) a dedicated view for an individual study participant, (3) the conceptualization of an AI simulation, and (4) a data entry page aligned with the study protocol.
The feedback on the mock-ups included the exclusion of real names given that all study participants would be collectively visible on one page in the mock-up.
JMIR Aging 2025;8:e66660
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