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Artificial Intelligence in Cardiology: Applications in Diagnosis and Risk Prediction
Anand Sekar G1, Ajit V Kulkarni2, Chetan Kumar Sharma3
1Department of Cardiology, Aarupadai Veedu Medical College, Vinayaka Missions Research Foundation (VMRF-DU), Kirumampakkam, IND.
Insights
Artificial intelligence (AI) offers advanced tools for diagnosing and predicting cardiovascular diseases (CVDs). Machine learning and deep learning improve accuracy and risk stratification, enhancing patient care.
Area of Science:
- Cardiology
- Medical Informatics
- Artificial Intelligence in Medicine
Background:
- Cardiovascular diseases (CVDs) remain a leading global cause of death and morbidity.
- Current diagnostic and predictive methods for CVDs have limitations in reflecting complex clinical interactions and dataset diversity.
- There is a growing need for advanced computational approaches in cardiovascular medicine.
Purpose of the Study:
- To review the latest developments in artificial intelligence (AI) for diagnosing and predicting cardiovascular risk factors.
- To focus on the clinical applications and challenges of AI in cardiology.
- To explore the potential of machine learning (ML) and deep learning (DL) in cardiovascular care.
Main Methods:
- A comprehensive literature review was conducted on AI applications in cardiovascular imaging, electrocardiography (ECG), and predictive modeling.
- Analysis of studies utilizing machine learning (ML) and deep learning (DL) techniques.
- Examination of AI integration with wearable technologies and multi-modal data sources.
Main Results:
- AI demonstrates improved diagnostic accuracy and earlier detection of cardiovascular diseases compared to traditional methods.
- AI enhances the ability to stratify cardiovascular disease risk, enabling more personalized patient management.
- Integration of AI with wearable devices facilitates continuous monitoring and proactive health management.
Conclusions:
- AI, including ML and DL, holds significant potential to revolutionize cardiovascular care through data-driven insights.
- Addressing challenges related to validation, ethics, and integration is crucial for widespread clinical adoption of AI in cardiology.
- The evolution of AI promises enhanced clinical decision-making, risk assessment, and improved patient outcomes in cardiovascular medicine.
Abstract:
Cardiovascular diseases (CVDs) continue to be a major cause of death and morbidity throughout the world, and there is an increasing need for better diagnostic and predictive approaches. Existing methods do not fully reflect complex clinical interactions, and new computational methods possess greater capabilities. The limitations, including a lack of diversity in datasets, low generalizability, and limited interpretability, limit general use in the clinic. The review highlights the latest developments in artificial intelligence (AI) for diagnosing and predicting cardiovascular risk factors, with a focus on its clinical applications and current challenges. A literature review was conducted on machine learning (ML) and deep learning (DL) applications in imaging, electrocardiography (ECG), and predictive modeling. AI has been shown to improve diagnostic accuracy, facilitate earlier diagnosis, and enhance the ability to stratify disease risk compared with traditional methods. The seamless integration with wearable technologies ensures continuous monitoring and proactive management. While these developments help to ensure more accurate and individualized care, there are issues of validation, ethics, and integration. Moreover, integration of multi-modal data sources and real-time analytics enhances clinical decision-making and risk assessment. As technology continues to evolve, its scalability and applicability across various healthcare settings are expected to improve. In summary, AI has the potential to revolutionize cardiovascular care and enhance clinical outcomes by leveraging data-driven approaches.
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