Current Advancements in Cardiovascular Disease Management using Artificial Intelligence and Machine Learning Models: Current Scenario and Challenges

Recently, Artificial Intelligence (AI) and Machine Learning (ML) have been significant contributors to the development of sophisticated systems and models for cardiovascular disease (CVD) management. There is a wide scope of applications available today. Electrocardiography (ECG), cardiovascular image analysis and wearables for remote monitoring are the most consistently developing domains that are beginning to be AI-driven as technology and software advance over time. Furthermore, throughout the COVID-19 pandemic, AI has been utilised for cardiovascular disease management, drug targeting and risk analysis. The aim of the review is to highlight the recent advancements related to CVD management using AI and ML-based technologies. The works of literature reviewed in the present study consider the advancements of AI and ML-based CVD management technologies developed in the past decade. The different methods and technologies were studied, the working of various AI and ML algorithms have been discussed and few research gaps have been identified. The current implementations of AI algorithms lay a foundation to reduce the time and effort required to develop newer systems for future developments. Subsequently, overcoming the limitations of data processing techniques, many parameters required, and standardized datasets will improve the accuracy, implementation and interoperability of the AI and ML-based technologies for seamless integration in healthcare.