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Algorithmic Classification of Psychiatric Disorder–Related Spontaneous Communication Using Large Language Model Embeddings: Algorithm Development and Validation

Algorithmic Classification of Psychiatric Disorder–Related Spontaneous Communication Using Large Language Model Embeddings: Algorithm Development and Validation

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.

Ryan Allen Shewcraft, John Schwarz, Mariann Micsinai Balan

JMIR AI 2025;4:e67369

Framing the Human-Centered Artificial Intelligence Concepts and Methods: Scoping Review

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.

Roberta Bevilacqua, Tania Bailoni, Elvira Maranesi, Giulio Amabili, Federico Barbarossa, Marta Ponzano, Michele Virgolesi, Teresa Rea, Maddalena Illario, Enrico Maria Piras, Matteo Lenge, Elisa Barbi, Garifallia Sakellariou

JMIR Hum Factors 2025;12:e67350

Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?

Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?

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.

Matthew Amer, Rosalind Gittins, Antonio Martinez Millana, Florian Scheibein, Marica Ferri, Babak Tofighi, Frank Sullivan, Margaret Handley, Monty Ghosh, Alexander Baldacchino, Joseph Tay Wee Teck

J Med Internet Res 2025;27:e58723

Striking a Balance: Innovation, Equity, and Consistency in AI Health Technologies

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.

Eric Perakslis, Kimberly Nolen, Ethan Fricklas, Tracy Tubb

JMIR AI 2025;4:e57421

Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study

Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study

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.

Heonyi Lee, Yi-Jun Kim, Jin-Hong Kim, Soo-Kyung Kim, Tae-Dong Jeong

J Med Internet Res 2025;27:e63983