The role of artificial intelligence and mobile health in diagnosis and management of pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is a rare, progressive disorder characterized by pulmonary vascular remodeling, increased pulmonary vascular resistance, and ultimately right ventricular failure. Despite therapeutic advances, delayed diagnosis and imprecise risk stratification remain key challenges. Artificial intelligence (AI) and machine learning (ML) offer opportunities across the care continuum. This includes early detection from electronic health records, electrocardiography and imaging, automated, standardized interpretation of echocardiography, computed tomography (CT), and cardiac magnetic resonance (CMR) potentially expediting referral and final diagnosis. Deep learning applied to echocardiography achieves expert-level PAH classification and provides automated right-heart quantification while CT/CMR-based models segment the heart and great vessels, quantify lung disease radiomics, and infer hemodynamics, supporting noninvasive triage and prognostication. In future, remote monitoring with wearables and telemedicine, coupled with AI analytics, promises to enable proactive management and potentially reduce hospitalizations. While early studies are promising, translation to practice requires rigorous external validation, prospective studies, bias auditing and seamless integration into guideline-aligned workflows. Overall, AI stands to augment expert clinical judgement by converting high-dimensional, multimodal data into actionable insights. With careful governance and evidence generation, AI has the potential to shorten time-to-diagnosis, refine risk stratification, and personalize therapy in PAH, ultimately improving outcomes for this high-risk population.