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Applications of AI in Predicting Drug Responses for Type 2 Diabetes

Applications of AI in Predicting Drug Responses for Type 2 Diabetes

AI includes a range of methods, among which ML and deep learning (DL) stand out as 2 prominent subsets [8]. ML is involved in building systems that are capable of learning from data, identifying patterns, and making decisions. On the other hand, DL, is a special form of ML inspired by the structure and function of the brain, especially neural networks. These models learn from data autonomously and are adaptable to various features.

Shilpa Garg, Robert Kitchen, Ramneek Gupta, Ewan Pearson

JMIR Diabetes 2025;10:e66831

Estimation of Static Lung Volumes and Capacities From Spirometry Using Machine Learning: Algorithm Development and Validation

Estimation of Static Lung Volumes and Capacities From Spirometry Using Machine Learning: Algorithm Development and Validation

A prior study by Beverin et al [7] examined the prediction of total lung capacity from spirometry using three tree-based machine learning (ML) models, achieving a mean squared error of 560.1 m L. They further developed models to classify restrictive ventilatory impairment, achieving a sensitivity and specificity of 83% and 92%, respectively. However, they did not explore prediction of the complete lung volume assessments.

Scott A Helgeson, Zachary S Quicksall, Patrick W Johnson, Kaiser G Lim, Rickey E Carter, Augustine S Lee

JMIR AI 2025;4:e65456

Public Disclosure of Results From Artificial Intelligence/Machine Learning Research in Health Care: Comprehensive Analysis of ClinicalTrials.gov, PubMed, and Scopus Data (2010-2023)

Public Disclosure of Results From Artificial Intelligence/Machine Learning Research in Health Care: Comprehensive Analysis of ClinicalTrials.gov, PubMed, and Scopus Data (2010-2023)

The number of studies on artificial intelligence/machine learning (AI/ML) has surged in recent years, exceeding prior expectations [1]. The growth of AI/ML research in health care continues to gain momentum, driven by its potential to enable early detection of serious conditions in resource-constrained settings or facilitate timely identification of patient deterioration that might otherwise go unnoticed, to name a few.

Shoko Maru, Ryohei Kuwatsuru, Michael D Matthias, Ross J Simpson Jr

J Med Internet Res 2025;27:e60148