AI-Driven Diagnostic Tools for Cardiovascular Risk Assessment Opportunities and Challenges

Abstract

Introduction: Important statistics show that cardiovascular diseases (CVDs) remain one of the world's top causes of death. The prevention of adverse outcomes depends on the combination of early diagnosis and efficient risk assessment. Traditional assessment models predict that the application of artificial intelligence (AI) techniques for diagnostic purposes will change cardiovascular risk assessment by improving accuracy and speed of projections.
Objectives: A comparative study between AI-based diagnostic tools and traditional risk assessment methods, like the Framingham Risk Score, is required.
Materials and Methods: The study was carried out at University of Lahore Teaching Hospital, Pakistan from January 2024 to June of 2024. The study examined patients using AI prediction models and conventional risk prediction techniques, focusing on individuals with cardiovascular risk factors.
Results: AI prediction models performed better when assessing cardiovascular risks to identify vulnerable patients, with 85% sensitivity and 80% specificity.
Conclusion: In order to improve patient outcomes, preventative cardiovascular healthcare systems are advanced by AI-powered analytical tools that offer accurate real-time risk assessment.