Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases

Area of Science:

• Cardiology
• Medical Informatics
• Artificial Intelligence

Background:

• Cardiovascular disease (CVD) is a leading global cause of death and disability.
• Challenges in CVD management include inadequate prevention, delayed diagnosis, inconsistent treatment application, and poor patient compliance.
• Existing healthcare systems struggle to manage the increasing burden of CVD effectively.

Purpose of the Study:

• To explore the role and potential of Artificial Intelligence (AI)-based Clinical Decision Support Systems (CDSSs) in improving cardiovascular disease care.
• To highlight how AI-CDSSs can address critical gaps in CVD prevention, diagnosis, and treatment.
• To emphasize the evolving landscape of AI applications in managing cardiovascular health.

Main Methods:

• Review of AI techniques applied to healthcare decision-making.
• Analysis of AI-CDSS functionalities in risk assessment, diagnosis, treatment optimization, and patient monitoring for CVD.
• Discussion on the integration of AI in clinical workflows for cardiovascular care.

Main Results:

• AI-based CDSSs provide accurate, personalized information to healthcare professionals.
• These systems enhance capabilities in risk stratification, early diagnosis, and tailored treatment strategies for CVD.
• AI facilitates continuous patient monitoring and early warning systems, crucial for managing chronic conditions like CVD.

Conclusions:

• AI-based CDSSs represent a significant advancement in addressing the complexities of cardiovascular disease management.
• Effective implementation requires high-quality data for training and rigorous evaluation by medical experts.
• The continued development and integration of AI hold substantial promise for reducing CVD morbidity and mortality worldwide.