e.g. mhealth
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Skip search results from other journals and go to results- 186 Journal of Medical Internet Research
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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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In order to effectively interact with AI-based chatbots and apply the CAPE framework, we used a persona-based approach. In this approach, the researcher interacts with the chatbot via a dynamic script representing a fictional client called a persona. The personas are written as client evaluations in the biopsychosocial framework, providing holistic information on the fictional client.
JMIR Form Res 2025;9:e65605
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Patients with lower health literacy may benefit from structured AI-generated summaries, potentially reducing anxiety and misunderstandings regarding their diagnosis. However, given the residual complexity in some explanations provided by GPT-4o, integrating human oversight in AI-assisted patient education remains crucial until further improvement in LLMs is seen in the future.
JMIR AI 2025;4:e69654
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Machine learning is a subfield of artificial intelligence (AI) that enables information technology systems to recognize patterns in existing data and develop solutions autonomously. This research involves classifying data quality variables for clinical data. The classification requires a training dataset with examples for each data source entity’s input and output variables.
All machine learning models and classifiers were developed in Python (Python Software Foundation) using scikit-learn libraries.
JMIR Med Inform 2025;13:e60204
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Sight Bot, a research chatbot, uses both Open AI and Pub Med’s application programming interface (APIs) to restrict the information available to GPT-3.5 [8]. This limits the data that the AI can access in the hopes that this will reduce “AI hallucination”–the fabrication of data [8]. Bio Med LM is built upon the Hugging Face GPT model with 2.7 billion parameters and is also trained upon biomedical data from Pub Med [9]. However, there is limited research on AI used as ophthalmological diagnostic tools.
JMIR Form Res 2025;9:e64986
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