Is There Potential for AI in Cardiovascular Disease Management?


Artificial intelligence (AI) is a branch of computer science that focuses on developing technologies that mimic the human mind process. Given the high burden of cardiovascular diseases (CVDs) in the population and recent advancements in big data analysis and cloud computing technologies, AI can potentially help in developing effective strategies for the diagnosis and treatment of CVDs. AI can be used in the following branches of cardiovascular medicine:

  • Precision medicine

AI can help in implementing remote patient follow-ups, reminding patients about medications, providing real-time disease counselling, and detecting early symptoms. From the clinicians’ perspective, AI can consolidate medical record systems and decrease workload. In the future, AI may help clinicians in designing customized healthcare plans for CVD patients.

  • Clinical predictions

AI can enable clinicians to predict patient outcomes more accurately. Studies have demonstrated that AI can predict survival rates and possible time periods of death in CVD patients, and also evaluate the risk of death among coronary heart disease patients.

  • Cardiac imaging analysis

Deep learning can help in analyzing coronary angiograms, echocardiograms, and electrocardiograms. In the future, AI has the potential to aid in identifying coronary atherosclerotic plaques, measuring the size of each heart chamber, assessing left ventricular function, and evaluating structural diseases like a valvular disease.

  • Intelligent robots

AI can greatly improve surgical outcomes like patient trauma and the duration of hospital stays. Cardiac interventional operations like percutaneous coronary intervention procedures and catheter ablations can be undertaken by AI, in turn reducing radiation exposure to clinicians from digital subtraction angiography. Through reinforcement learning, AI can learn how to perform more rapid cardiac operations.

AI has the potential to secure a foothold in cardiovascular medicine and can complement clinicians’ work in diagnosing and managing patients with CVDs to improve their outcomes.