⚡ Quick Summary
This review highlights the transformative role of artificial intelligence (AI) in predicting cardiovascular disease (CVD) risk, emphasizing its potential to enhance accuracy and efficiency in risk assessment. AI-driven tools have demonstrated significant improvements in mortality rates and quality of life (QOL)
🔍 Key Details
- 📊 Focus: Integration of AI in cardiovascular risk prediction
- 🧩 Technologies: Machine Learning (ML) and Deep Learning (DL)
- ⚙️ Applications: ECG analysis, survival rate prediction, hypertension risk forecasting
- 🏆 Accuracy: 91.7% for hypertension risk prediction using DL
🔑 Key Takeaways
- 💡 AI enhances the accuracy of cardiovascular risk prediction models.
- 🤖 Machine learning algorithms have improved 2-year survival rate predictions after myocardial infarction (MI).
- 📈 Deep learning achieved a remarkable 91.7% accuracy in forecasting hypertension risk.
- 🩺 AI-driven ECG analysis can identify left ventricular systolic dysfunction with high precision.
- 🌍 Adoption challenges exist due to a lack of education and acceptance among healthcare professionals.
- 📚 Education is crucial for promoting AI’s benefits in cardiovascular risk assessment.
- 🏥 Personalized treatment strategies can be developed through AI integration.
📚 Background
Cardiovascular disease remains the leading cause of mortality globally, highlighting the urgent need for effective prevention and management strategies. Traditional risk assessment tools often fall short, particularly in complex patient profiles and atypical presentations. This gap underscores the necessity for advanced approaches, particularly those that incorporate artificial intelligence to enhance risk prediction accuracy and efficiency.
🗒️ Study
This review synthesizes current understanding and future research directions regarding the role of AI in cardiovascular disease risk prediction. It explores various AI technologies, including machine learning and deep learning, and their applications in improving patient outcomes through more accurate risk assessments.
📈 Results
The implementation of AI-driven tools has shown promising results, with machine learning algorithms predicting 2-year survival rates after myocardial infarction with improved accuracy compared to traditional models. Deep learning techniques have forecasted hypertension risk with an impressive accuracy of 91.7%, and AI-driven ECG analysis has demonstrated high precision in identifying left ventricular systolic dysfunction, even from noisy data collected via wearable devices.
🌍 Impact and Implications
The integration of AI in cardiovascular risk assessment has the potential to revolutionize patient care by enabling more personalized treatment strategies and fostering greater patient engagement. However, the widespread adoption of these technologies faces challenges, primarily due to a lack of education and acceptance among healthcare professionals. Promoting understanding and acceptance of AI’s benefits is crucial for accelerating its integration into clinical practice, ultimately aiming to mitigate the global impact of cardiovascular disease.
🔮 Conclusion
This review underscores the incredible potential of artificial intelligence in enhancing cardiovascular disease risk prediction. By improving accuracy and efficiency, AI can lead to better patient outcomes and reduced healthcare costs. The future of cardiovascular care looks promising with the continued integration of AI technologies, and further research is essential to unlock their full potential.
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The Role of Artificial Intelligence in Cardiovascular Disease Risk Prediction: An Updated Review on Current Understanding and Future Research.
Abstract
Cardiovascular disease (CVD) Continues to be the leading cause of mortality worldwide, underscoring the critical need for effective prevention and management strategies. The ability to predict cardiovascular risk accurately and cost-effectively is central to improving patient outcomes and reducing the global burden of CVD. While useful, traditional tools used for risk assessment are often limited in their scope and fail to adequately account for atypical presentations and complex patient profiles. These limitations highlight the necessity for more advanced approaches, particularly integrating artificial intelligence (AI) into cardiovascular risk prediction. Our review explores the transformative role of AI in enhancing the accuracy, efficiency, and accessibility of cardiovascular risk prediction models. The implementation of AI-driven risk assessment tools has shown promising results, not only in improving CVD mortality rates but also in enhancing quality of life (QOL) markers and reducing healthcare costs. Machine learning (ML) algorithms predicted 2-year survival rates after MI with improved accuracy compared to traditional models. Deep Learning (DL) forecasted hypertension risk with a 91.7% accuracy based on electronic health records. Furthermore, AI-driven ECG (Electrocardiography) analysis has demonstrated high precision in identifying left ventricular systolic dysfunction, even with noisy single-lead data from wearable devices. These tools enable more personalized treatment strategies, foster greater patient engagement, and support informed decision-making by healthcare providers. Unfortunately, the widespread adoption of AI in CVD risk assessment remains a challenge, largely due to a lack of education and acceptance among healthcare professionals. To overcome these barriers, it is crucial to promote broader education on the benefits and applications of AI in cardiovascular risk prediction. By fostering a greater understanding and acceptance of these technologies, we can accelerate their integration into clinical practice, ultimately aiming to mitigate the global impact of CVD.
Author: [‘Tiwari A’, ‘Shah PC’, ‘Kumar H’, ‘Borse T’, ‘Arun AR’, ‘Chekragari M’, ‘Ochani S’, ‘Shah YR’, ‘Ganesh A’, ‘Ahmed R’, ‘Sharma A’, ‘Mylavarapu M’]
Journal: Curr Cardiol Rev
Citation: Tiwari A, et al. The Role of Artificial Intelligence in Cardiovascular Disease Risk Prediction: An Updated Review on Current Understanding and Future Research. The Role of Artificial Intelligence in Cardiovascular Disease Risk Prediction: An Updated Review on Current Understanding and Future Research. 2025; (unknown volume):(unknown pages). doi: 10.2174/011573403X351048250329170744