Impact of artificial intelligence and digital twin technology on cardiovascular disease diagnosis and management challenges and future directions (Review)

The incidence of cardiovascular disease (CVD) is rising steadily and continues to be the major cause of mortality worldwide. The pressing requirement is to develop personalised healthcare solutions. Digital twin (DT) and artificial intelligence (AI) technology can change the treatment of CV through personal disease modelling, risk stratification, diagnosis and prediction. AI‑powered DT technologies develop patient‑specific simulations that aid in early diagnosis, optimized treatment and post‑intervention monitoring. Machine learning algorithms and deep neural networks enable real‑time data identity from electronic health records, portable sensors and medical imaging to continuously update digital twins to represent physiological changes. AI‑powered DT models also help in better clinical decision‑making by modelling disease progression and accurately predicting treatment outcomes. However, its universal adoption is hampered by issues of data privacy concerns, computational power requirements, and regulatory compliance. Strengthening these capabilities using good data stewardship, interdisciplinarity and next‑generation computational architectures will accelerate the use of DT technology in cardiovascular medicine. The present review emphasizes the applications of AI‑based DT models to correct the future of accurate cardiology, pursue the patient's results and reduce the burden of health care.