Artificial Intelligence and Multi-Omics Approaches in the Precision Management of Pulmonary Hypertension: From Early Diagnosis to Therapeutic Stratification.
Authors
Affiliations (8)
Affiliations (8)
- Molecular and Clinical Medicine PhD Program, University of Palermo, 90127 Palermo, Italy.
- Cardiology Unit, San Vito e Santo Spirito Hospital, 91011 Alcamo, Italy.
- Cardiology Unit, Cervello Hospital, A.O. Ospedali Riuniti Villa Sofia-Cervello, 90146 Palermo, Italy.
- Medicine Unit, Fondazione G. Giglio, 90015 Cefalù, Italy.
- Department of Internal Medicine, S. Elia Hospital, 93100 Caltanissetta, Italy.
- Department of Internal Medicine, Buccheri La Ferla Hospital, 90123 Palermo, Italy.
- Department of Medicine and Surgery, "Kore" University of Enna, 94100 Enna, Italy.
- Department of Pulmonary Medicine, S. Elia Hospital, 93100 Caltanissetta, Italy.
Abstract
Pulmonary hypertension (PH) is a heterogeneous clinical syndrome in which similar haemodynamic abnormalities may arise from distinct vascular, cardiac, pulmonary, thromboembolic, and molecular mechanisms. This complexity limits the ability of conventional classifications and risk scores to fully capture individual disease trajectories and treatment responses. Artificial intelligence (AI) offers a framework for integrating clinical data, electrocardiography, multimodal imaging, invasive haemodynamics, biomarkers, and multi-omics information across the PH care pathway. This review summarises current applications of machine learning and deep learning in early detection, diagnostic referral, right-ventricular and pulmonary vascular phenotyping, molecular endotyping, risk stratification, and therapeutic decision support. Available studies show promising results for AI-assisted electrocardiographic screening, automated echocardiographic and cardiac magnetic resonance analysis, computed tomography (CT)-based phenotyping, and multimodal prognostic modelling. True multi-omics integration in PH remains limited to discovery studies and has not yet yielded externally validated endotype or treatment-response classifiers. Evidence maturity is task-dependent: screening and phenotyping span several PH groups, whereas validated risk tools, molecular endotyping, and pathway-directed therapy remain predominantly PAH-based, particularly in idiopathic/heritable PAH. However, most evidence remains retrospective, derives from selected referral populations, and lacks robust external or prospective validation. No AI-based model currently supports routine drug selection or autonomous clinical decision-making. Future progress will require harmonised multicentre datasets and standardised acquisition protocols, transparent and interpretable models, and prospective studies demonstrating meaningful clinical benefit. AI should therefore be viewed as an emerging decision-support tool that may strengthen precision medicine in PH while complementing clinical expertise across diagnosis, phenotyping, risk assessment, and therapeutic stratification pathways.