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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Minibatch gradient descent with the ADAM optimization algorithm was used with a weighted categorical cross-entropy loss (batch size=1×100 consecutive frames, learning rate 10−4, beta1=.9, beta2=.999, epsilon=10−8). To mitigate the effects of unbalanced category distributions, each frame’s error in the loss functions was weighted with the inverse probability of the target class occurrence.
JMIR Mhealth Uhealth 2025;13:e58078
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From the included studies, the following data elements were then extracted: study design; sample size; skin types included; length of study for each participant; location of study; materials used (such as types of allergen panels and imaging equipment); type of AI algorithm and its performance in the study; limitations and challenges of the study; and future directions.
JMIR Dermatol 2025;8:e67154
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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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