e.g. mhealth
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Skip search results from other journals and go to results- 39 JMIR Medical Informatics
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For the classification task, we used the Extreme Gradient Boosting (XGBoost) algorithm, using a multiclass classifier with a softmax objective function to predict the class labels for the posts from the 7 psychiatric disorder subreddits. Given that tree-based methods are not sensitive to the scale of the input features, we did not perform any standardization or normalization of the embeddings before using XGBoost.
JMIR AI 2025;4:e67369
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Framing the Human-Centered Artificial Intelligence Concepts and Methods: Scoping Review
Fagbola et al [12] discussed tools such as Fair ML and IBM AI Fairness 360 to ensure algorithm interpretability and transparency. In addition, integrating AI into clinical workflows poses a challenge. Ventura et al [17] explored co-design processes with poststroke patients and caregivers, demonstrating that user involvement enhances AI solution acceptance and facilitates integration into care pathways.
JMIR Hum Factors 2025;12:e67350
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Furthermore, with regard to function, it is important for clinicians and patients to understand the limits of these predictions, including the data used for training the algorithm and the implications for both accepting and rejecting the output [84]. Unfortunately, the lack of algorithmic transparency is a common finding.
J Med Internet Res 2025;27:e58723
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Striking a Balance: Innovation, Equity, and Consistency in AI Health Technologies
In the United States, the product type can vary from a device software or algorithm that may be classified as mobile medical apps, software functions that are not medical devices, clinical decision support software, or software as a medical device (Sa MD) [24-26]. Each of these product types needs different types and levels of evidence to support them in the market and may need regulatory approval.
JMIR AI 2025;4:e57421
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Are Dating App Algorithms Making Men Lonely and Does This Present a Public Health Concern?
algorithm
JMIR Form Res 2025;9:e70594
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The purpose of this study was to develop an algorithm using ML techniques to forecast whether the initial vancomycin regimen to be administered can achieve an AUC24/MIC ratio within the therapeutic range. In other words, the final output of the ML algorithm predicted “yes” or “no” based on whether the AUC24/MIC of vancomycin falls within the therapeutic range of 400 to 600.
J Med Internet Res 2025;27:e63983
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