Clinical integration of artificial intelligence for diagnosis, risk prediction and management of rare cardiovascular diseases.
Authors
Affiliations (6)
Affiliations (6)
- Department of Pharmacology, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India.
- Centre for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, Tamil Nadu, India.
- Department of Paediatrics, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India.
- Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
- Department of Orthopaedics, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India.
- Saveetha Institute of Basic Medical Sciences (SIBMS), Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India.
Abstract
RCDs comprise a diverse group of inherited and acquired disorders characterized by low prevalence, substantial genetic and phenotypic heterogeneity, delayed diagnosis, and limited evidence-based therapeutic options. These challenges contribute to poor clinical outcomes, including progressive heart failure, malignant arrhythmias, and sudden cardiac death. This review summarizes recent advances in artificial intelligence (AI) for the diagnosis and management of RCDs. AI technologies, including machine learning, deep learning, and multimodal data integration, enable comprehensive analysis of electrocardiography, echocardiography, cardiac magnetic resonance imaging, computed tomography, genomic and multi-omics data, biomarkers, wearable devices, and electronic health records. These approaches improve early disease detection, phenotypic classification, risk stratification, prognostic prediction and personalized treatment planning. Current evidence suggests that AI significantly enhances diagnostic accuracy, prognostic assessment, and individualized clinical decision-making in patients with RCDs. Future clinical implementation requires standardized multicenter datasets, prospective validation, transparent and explainable algorithms, robust regulatory and ethical frameworks, and seamless integration into healthcare systems. Continued interdisciplinary collaboration will accelerate AI-driven precision cardiovascular medicine and improve outcomes for patients with rare cardiovascular diseases.