What is the clinical evidence from this study?

Study design: Review. Population: Cardiovascular Disease. Intervention: Artificial Intelligence algorithms vs. Conventional data analysis methods.

What does this research mean for the field?

Artificial intelligence algorithms significantly enhance the diagnosis, classification, and prognostic prediction of cardiovascular diseases by analyzing complex healthcare data more effectively than traditional methods. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

May 27, 2025Open Access

An in-Depth Review of Cardiovascular Disease Prognosis Using Algorithms Based Upon Artificial Intelligence

Key Result

Artificial intelligence algorithms demonstrate significant potential in improving the timely diagnosis, prognosis, and risk prediction of cardiovascular diseases compared to conventional methods.

Structured PICO

Do Artificial Intelligence and Machine Learning algorithms improve the accuracy of cardiovascular disease diagnosis and prognosis compared to traditional methods?

Population

Patients with or at risk for cardiovascular diseases across 92 reviewed papers utilizing various healthcare datasets (e.g., UCI, Kaggle).

Intervention

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) algorithms for cardiovascular disease prognosis and diagnosis.

Comparator

Traditional data analysis methods and conventional medical procedures.

Outcome

Algorithmic performance metrics including sensitivity, precision, accuracy, F1-score, specificity, and AUC for identifying cardiovascular diseases.

Artificial intelligence and machine learning algorithms show significant promise in enhancing the accuracy and efficiency of cardiovascular disease diagnosis, risk stratification, and prognosis.

Limitations

Data bias in training algorithms can lead to algorithmic unfairness and discrimination.
Risk of adversarial attacks using fraudulent inputs to alter machine learning algorithms.
Lack of randomized controlled trials to verify the clinical efficacy and therapeutic impact of AI algorithms.
Dearth of research available specifically for the prediction of cardiovascular disease in middle-aged people.

Abstract

High mortality rates are a result of cardiovascular diseases (CVDs), which pose serious global health challenges. It is possible to decrease the risk of having an acute myocardial infarction and the mortality rate among people with cardiovascular diseases by promptly detecting cardiovascular events. The multifaceted pathological techniques and diverse determinants engaged in the menace assessment of CVDs, heart attacks, interpretations of medical imaging, therapeutic decisionmaking, and diagnosis of disease necessitate revisions to conventional data analysis methods. Artificial intelligence (AI) is a term used to describe technology that uses complex computer algorithms to analyse massive amounts of data. AI is now widely used in the medical field. AI methods have proven to be able to diagnose and treat a variety of CVDs more quickly, including hypertrophic cardiomyopathy, congenital heart disease, valvular heart disease, atrial fibrillation, and heart failure. We examined 92 papers from reliable sources, including Google Scholar, Springer, Elsevier, and others, for this thorough review. AI has shown great promise in clinical settings for the diagnosis of cardiovascular diseases, the improvement of supporting tools, the classification and stratification of disorders, and the prediction of outcomes. Intellectual AI systems have been carefully designed to examine complicated relationships in large amounts of healthcare data, enabling them to perform more complex jobs than traditional methods.

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