Intelligent Physiology: Integrating Artificial Intelligence from Molecular Discovery to Clinical Decision Support

Export Artificial intelligence (AI) is transforming physiological sciences by connecting molecular discovery, biomarker identification, real-time physiological signal analysis, and clinical decision support. This review highlights four major domains where AI has achieved transformative progress, bridging this continuum. In structure-guided and generative therapeutics, models such as AlphaFold and deep generative design frameworks enable atomic-level prediction of protein structures, ligand docking, and automated synthesis planning. In biomarker discovery, deep learning applied to proteomic, metabolomic, glycomic, and lipidomic data expands molecular annotation and reveals disease-associated signatures beyond conventional detection limits. AI-driven systems for wearable and implantable monitoring now translate continuous physiological signals into real-time cardiovascular, respiratory, and metabolic insights, advancing precision health assessment. Finally, clinical decision-support frameworks and patient digital twins integrate predictive modeling and mechanistic constraints with evidence reasoning to enhance medical interpretation. Collectively, these advances illustrate how AI has evolved from analytical computation to mechanistic physiological understanding, inaugurating an era of “Intelligent Physiology” that links molecular mechanisms with individualized clinical care.