Key result
AI models achieve high accuracy in early CVD prediction but face ongoing interpretability and diversity challenges.
Why the study?
Reliable prediction and early warning systems for cardiovascular disease risk assessment are needed, but AI-based approaches face challenges including data diversity, model explainability, computational efficiency, and adaptability.
AI-based predictive models hold promise for highly precise and automated cardiovascular risk assessment, but require improvements in explainability, reliability, and real-time adaptability for practical healthcare deployment.
AI CVD prediction models need diverse data and explainability before clinical use; leaves open validation across populations.
Cardiovascular Diseases (CVD) have become one of the top health concerns in the world and there arises the need for a reliable way to predict CVD risk assessment and implement prevention management.With the use of recent advancements in the field of Artificial Intelligence (AI), the analysis of clinical data, Electrocardiogram (ECG) signals, biomarkers and physiological measurements used to enhance the predictive capacity of the cardiovascular system.However, the problems associated with data diversity, model explain ability, computational efficiency, real-time adaptability and transferability between patients remain challenges for AI-based approaches.The survey aims to assess new trends in AI-enabled cardiac diseases prediction and early warning through an analysis of 20 researches within the year 2024-2025.Methods include feature selection methods, Machine Learning predictive models, Ensemble learning, Deep learning approaches, Hybrid AI models, Attention models, Quantum learning and Explainable AI approaches.The comparative discussion can revolve around the contributions, performances and limitations of current risk assessment methods.As seen from the analysis above, there is the promise of using AI models to achieve high precision and automatic feature learning; nonetheless, it is equally important to note that future system requires robust and adaptive intelligence architectures to make sound clinical decisions.This can be seen in the current research in that there is the potential to improve the efficacy of next generation EWS in cardiovascular conditions by adding explainability and personalization.Utilization of AI in Healthcare Analytics can be able to change the process of predicting cardiovascular risk factors by being able to identify these problems in the initial stages and thus improve the efficiency of healthcare delivery systems and patient outcomes.AIs ability in Healthcare Analytics can be a great use in identifying cardiovascular risk factors.
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A.MEENA et al. (2026) conducted a review in Cardiovascular Disease. Artificial Intelligence Prediction Models was evaluated on Cardiovascular disease prediction accuracy and model performance. Artificial intelligence models, including machine learning and deep learning, demonstrate high accuracy in early cardiovascular disease prediction but face challenges with data diversity and clinical interpretability.
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