Artificial Intelligence in Physiology and Pathophysiology: A Tool for Mechanistic Insights and Translational Opportunities

Export Artificial intelligence (AI) has rapidly evolved into a powerful framework for analyzing complex and heterogeneous datasets in physiological and pathophysiological research. By leveraging advances in machine learning and deep learning, AI enables the detection of subtle patterns in physiological signals, the automated interpretation of high-dimensional biomedical images, and the integration of multi-omics profiles with functional phenotypes. This mini review highlights current key developments, covering applications in signal analysis, imaging, computational modeling, and translational case studies that link mechanistic understanding to potential clinical interventions. Examples include AI-driven arrhythmia detection, microvascular image quantification, and hybrid physics–AI simulations for personalized medicine. Current challenges – such as data quality, population bias, and model interpretability – are discussed alongside emerging solutions, including self-supervised learning, federated analytics, and the development of physiological digital twins. Collectively, these advances underscore AI’s growing role as both a discovery engine and a translational bridge, offering opportunities to accelerate mechanistic insights and improve patient-centered outcomes in physiology and disease research.