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Skip search results from other journals and go to results- 37 JMIR Medical Informatics
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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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The algorithm takes as input the 3 D ultrasound image and outputs the corresponding predicted segmentation. During development, the algorithm learned to set its internal parameters by minimizing the difference between the predicted segmentation and the segmentations obtained in VR. Two separate models were developed: one for segmenting the embryo and another for the embryonic head.
J Med Internet Res 2025;27:e60887
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Additionally, we trained an e Xtreme gradient boosting (XGBoost) algorithm [42] to predict 5 CCP subgroups with differential risks of outcomes, as described in our previous work [23]. Briefly, the model was trained and calibrated using an isotonic regression algorithm, and internally validated in the discovery cohort. The SHapley Additive ex Planations (SHAP) method was employed to identify each feature’s relative contribution [23,43] and enhance the model’s explainability.
JMIR Public Health Surveill 2025;11:e67840
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In the second part, we fine-tuned the sentence transformer and then used the Doc SCAN algorithm to cluster the synthetic datasets. We chose the sbert-chinese-general-v2 model, which is a model pretrained on the Sim CLUE dataset [30], as the base model due to its outstanding performance on embedding Chinese sentences.
JMIR Form Res 2025;9:e54803
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