Abstract
Pulmonary hypertension (PH) is a progressive cardiopulmonary disorder associated with high morbidity and mortality. Delayed diagnosis, heterogeneous disease phenotypes, and variable treatment responses remain major clinical challenges. Artificial intelligence (AI), including machine learning and deep learning, has emerged as a promising tool for improving PH management. AI-based models have demonstrated promising performance in screening, diagnosis, classification, risk stratification, and prognosis prediction using multimodal data such as electrocardiography, chest radiography, computed tomography, cardiac magnetic resonance imaging, echocardiography, and electronic health records. Furthermore, AI may support individualized risk assessment, treatment-response prediction, early identification of right ventricular deterioration, and longitudinal remote monitoring. Emerging technologies, including explainable AI, federated learning, large language models, and digital twins, may further advance precision medicine in PH. Despite these advances, data quality, interpretability, privacy, and clinical implementation of AI remain challenges. This review summarizes current applications, opportunities, and challenges of AI in PH and discusses future directions for AI-assisted precision healthcare.