e.g. mhealth
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Personalization of AI Using Personal Foundation Models Can Lead to More Precise Digital Therapeutics
Conventional ML methodologies typically involve training a single generalized model to classify a specific condition [11], such as for diagnostic or screening purposes. However, attempting to apply a universal model often leads to poor generalization to individuals and health systems that were not represented in the training data. An alternative solution involves training separate models for each individual or subgroup, tailoring the model to the unique characteristics of the patient.
JMIR AI 2025;4:e55530
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Park et al [17] used an oversampling technique to address data imbalance and predicted colorectal cancer chemotherapy based on data from the Gil Medical Center in Korea using a deep learning model. Kwon et al [18] used machine learning models, such as the gradient boosted model, the distributed random forest, the generalized linear model, and the deep neural network, for a stacking ensemble. They diagnosed breast cancer using the best-performing model in the stacking ensemble.
JMIR Med Inform 2025;13:e75022
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Our project examined whether a small dataset for common colon polyp entities could be used to develop a robust and accurate ML model for diagnostic purposes using an Auto ML model from Google’s Vertex AI. Colon polyps are a precursor to invasive carcinoma, and a high-volume sample is encountered in the pathology lab.
JMIR Form Res 2025;9:e67457
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We then compared the results of each model in a table that included accuracy, precision, recall, and scoring.
Multiple machine learning algorithms were evaluated for predicting the quality of echocardiography data, with LR serving as the baseline model. LR achieved an accuracy of 73.0% (0.73/1), precision of 57.1% (0.571/1), recall of 73.0% (0.73/1), F1-score of 64.1% (0.641/1), and an AUC-ROC score of 52.4% (0.524/1).
JMIR Med Inform 2025;13:e60204
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A Companion Robot for Children With Asthma: Descriptive Development and Feasibility Pilot Study
In-depth interviews were conducted with a semistructured format to gain information about caregivers’ perceptions of the asthma management app and the pediatric asthma model. This approach facilitated a nuanced exploration of caregivers’ attitudes, including barriers and facilitators to app. The interview consisted of 7 main questions developed to prompt discussion on previous experiences with similar technologies, willingness to use the asthma model, and opinions on the model’s design.
JMIR Pediatr Parent 2025;8:e68943
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Reference 12: Validation of an organizational management model of remote implantable cardioverter-defibrillatormodel
JMIR Med Inform 2025;13:e66066
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Summary of articles included in this comprehensive review of artificial intelligence in patch testing.
a Genetic Algo Rithm for biomarker selection in high-dimensional Omics with RF-based classifier.
b Tuning set refers to a subset of data used to fine-tune the parameters of a machine learning model. In this study, the tuning set was used to optimize the hyperparameters of RF and LR models before final evaluation on the validation dataset.
JMIR Dermatol 2025;8:e67154
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The outcome of this fine-tuning process is a model that provides a high level of sensitivity and specificity in classifying and differentiating various types of randomized trial publications.
In our study, we initially established a baseline model for classifying publications using traditional machine learning and word embedding techniques to demonstrate the effectiveness of employing a transformer-based model in identifying publications based on nested designs.
JMIR Med Inform 2025;13:e63267
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The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to evaluate the risk of bias and applicability of all included studies [8]. PROBAST assesses risk of bias across 4 domains: study participants, predictors, outcomes, and statistical analysis, while applicability is evaluated through the first 3 domains.
JMIR Med Inform 2025;13:e64963
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transformer-empowered healthcare conversations: current trends, challenges, and future directions in large language model-enabledmodel
JMIR Dermatol 2025;8:e67551
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