Journal of Medical Internet Research

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Leveraging AI to Drive Timely Improvements in Patient Experience Feedback: Algorithm Validation

This study demonstrates the broader implementation and iteration of this algorithm in different hospital settings in England that use FFT to capture patient feedback. The implementation of the FFT free-text algorithm demands thorough testing to ensure robustness, accuracy, and applicability.

Mustafa Khanbhai, Catalina Carenzo, Sarindi Aryasinghe, David Manton, Erik Mayer

JMIR Med Inform 2025;13:e60900

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Proposal for Using AI to Assess Clinical Data Integrity and Generate Metadata: Algorithm Development and Validation

Based on the predictive quality approach further described by Tercan et al [12] and Schmitt et al [13], this research aimed to identify a predictive model-based quality algorithm for clinical data, including RWD, and provide automated quality inspection. The goal of this paper is to demonstrate the varying quality of medical data in primary clinical source systems and to inform researchers about data reliability using machine learning techniques.

Caroline Bönisch, Christian Schmidt, Dorothea Kesztyüs, Hans A Kestler, Tibor Kesztyüs

JMIR Med Inform 2025;13:e60204

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Navigating the Maze of Social Media Disinformation on Psychiatric Illness and Charting Paths to Reliable Information for Mental Health Professionals: Observational Study of TikTok Videos

The platform’s “For You” tab was used, which curates content using Tik Tok’s in-house recommendation algorithm based on popular topics, user engagement, and relevance, to gather the sample of 1000 Tik Tok videos. This strategy was chosen to represent the kind of content that users are most likely to come across naturally.

Alexandre Hudon, Keith Perry, Anne-Sophie Plate, Alexis Doucet, Laurence Ducharme, Orielle Djona, Constanza Testart Aguirre, Gabrielle Evoy

J Med Internet Res 2025;27:e64225

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Alert Reduction and Telemonitoring Process Optimization for Improving Efficiency in Remote Patient Monitoring Programs: Framework Development Study

“Alert fatigue” is an important barrier when developing a telemonitoring algorithm [10]. In hypertension management, for example, it is known that health care providers, on average, only respond to around 60% of generated off-target blood pressure (BP) alerts [11]. To improve the efficiency of a telemonitoring program, technical improvements or algorithm enhancement, such as designing more sophisticated alerts or refining threshold values, can be initiated [12,13].

Job van Steenkiste, Niki Lupgens, Martijn Kool, Daan Dohmen, Iris Verberk-Jonkers

JMIR Med Inform 2025;13:e66066

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Trade-Offs Between Simplifying Inertial Measurement Unit–Based Movement Recordings and the Attainability of Different Levels of Analyses: Systematic Assessment of Method Variations

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.

Manu Airaksinen, Okko Räsänen, Sampsa Vanhatalo

JMIR Mhealth Uhealth 2025;13:e58078

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Artificial Intelligence in Patch Testing: Comprehensive Review of Current Applications and Future Prospects in Dermatology

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.

Hilary S Tang, Joseph Ebriani, Matthew J Yan, Shannon Wongvibulsin, Mehdi Farshchian

JMIR Dermatol 2025;8:e67154

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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

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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.

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