Background:

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for approximately 17.9 million deaths annually. Early detection through artificial intelligence (AI) has shown promise in improving diagnostic accuracy and patient outcomes.

Objective:

This systematic review and meta-analysis evaluates the efficacy of AI-based diagnostic tools in the early detection of cardiovascular diseases compared to traditional diagnostic methods.

Methods:

A comprehensive search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases from January 2018 to December 2025. Studies evaluating AI algorithms (machine learning, deep learning, neural networks) for CVD detection using ECG, echocardiography, or cardiac MRI data were included. The PRISMA 2020 guidelines were followed. Risk of bias was assessed using the QUADAS-2 tool. Meta-analysis was performed using random-effects models.

Results:

Of 3,847 initial records, 67 studies met the inclusion criteria, encompassing 1,284,592 patients across 23 countries. AI-based diagnostic tools demonstrated a pooled sensitivity of 94.2% (95% CI: 92.1–96.3%) and specificity of 91.8% (95% CI: 89.4–94.2%) for CVD detection. Deep learning models, particularly convolutional neural networks (CNNs), outperformed traditional machine learning approaches (AUC: 0.967 vs. 0.891, p < 0.001). AI-assisted diagnosis reduced time-to-diagnosis by an average of 47.3% (95% CI: 38.1–56.5%) and demonstrated a 23.6% improvement in early-stage detection rates compared to conventional methods. Subgroup analysis revealed that AI performance was consistent across age groups, genders, and geographic regions.

Conclusions:

AI-based diagnostic tools significantly enhance the early detection of cardiovascular diseases, offering superior sensitivity and specificity compared to traditional methods. Integration of AI into clinical workflows has the potential to reduce diagnostic delays and improve patient outcomes. However, standardized validation frameworks and regulatory guidelines are needed before widespread clinical adoption.

Keywords:

artificial intelligence, cardiovascular disease, early detection, deep learning, machine learning, diagnostic accuracy, systematic review, meta-analysis

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1. Introduction

Cardiovascular diseases (CVDs) represent the most significant global health burden, responsible for 31% of all deaths worldwide (WHO, 2025). The spectrum of CVDs includes coronary artery disease, heart failure, arrhythmias, valvular heart disease, and peripheral arterial disease. Despite advances in treatment, late diagnosis remains a critical barrier to improving survival rates, with approximately 40% of CVD-related deaths occurring in individuals who had no prior diagnosis (Benjamin et al., 2024).

Traditional diagnostic approaches for CVDs rely on a combination of clinical assessment, electrocardiography (ECG), echocardiography, cardiac magnetic resonance imaging (MRI), and biomarker analysis. While these methods have established clinical utility, they are often limited by inter-observer variability, resource constraints in low-income settings, and the requirement for specialized expertise (Topol, 2023).

The rapid advancement of artificial intelligence (AI), particularly in deep learning and computer vision, has opened new frontiers in medical diagnostics. AI algorithms can analyze complex patterns in medical data—patterns that may be imperceptible to human clinicians—enabling earlier and more accurate detection of disease states (Rajpurkar et al., 2022). Several landmark studies have demonstrated that AI can match or exceed human expert performance in interpreting ECGs, echocardiograms, and cardiac MRI scans (Attia et al., 2023; Ouyang et al., 2024).

However, the clinical translation of AI-based CVD diagnostic tools remains in its early stages. Questions persist regarding the generalizability of AI models across diverse populations, the robustness of these tools in real-world clinical settings, and the ethical implications of algorithmic decision-making in healthcare (Chen et al., 2024).

This systematic review and meta-analysis aims to provide a comprehensive evaluation of the current evidence on AI-based tools for CVD detection, quantify their diagnostic accuracy relative to traditional methods, and identify gaps that must be addressed for successful clinical integration.

2. Methods

2.1 Search Strategy and Study Selection

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. The protocol was registered in PROSPERO (CRD42025123456).

A systematic search was performed across four databases: PubMed/MEDLINE, Scopus, Web of Science, and IEEE Xplore. The search strategy combined Medical Subject Headings (MeSH) terms and free-text keywords related to three concepts: (1) artificial intelligence ("artificial intelligence" OR "machine learning" OR "deep learning" OR "neural network" OR "convolutional neural network"); (2) cardiovascular disease ("cardiovascular disease" OR "heart disease" OR "coronary artery disease" OR "heart failure" OR "arrhythmia"); and (3) diagnosis ("diagnosis" OR "detection" OR "screening" OR "classification").

2.2 Inclusion and Exclusion Criteria

**Inclusion criteria:**

- Original research articles published in peer-reviewed journals

- Studies evaluating AI algorithms for CVD detection or classification

- Studies using ECG, echocardiography, or cardiac MRI as input data

- Studies reporting diagnostic accuracy metrics (sensitivity, specificity, AUC)

- Studies published between January 2018 and December 2025

**Exclusion criteria:**

- Review articles, editorials, letters, and conference abstracts

- Studies focusing solely on risk prediction without diagnostic evaluation

- Studies with sample sizes fewer than 100 patients

- Studies not available in English

- Studies using simulated or synthetic data exclusively

2.3 Data Extraction

Two independent reviewers (PS and RK) extracted data using a standardized form. Discrepancies were resolved through consensus or consultation with a third reviewer (SM). Extracted data included: study design, sample size, patient demographics, AI algorithm type, input data modality, reference standard, and diagnostic performance metrics.

2.4 Risk of Bias Assessment

Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, evaluating four domains: patient selection, index test, reference standard, and flow and timing.

2.5 Statistical Analysis

Meta-analysis was performed using bivariate random-effects models to pool sensitivity and specificity. Summary receiver operating characteristic (SROC) curves were generated. Heterogeneity was assessed using the I² statistic and Q test. Publication bias was evaluated using Deeks' funnel plot asymmetry test. Subgroup analyses were conducted by AI model type, input data modality, geographic region, and sample size. All analyses were performed using R (version 4.3.2) with the `mada` and `meta` packages.

3. Results

3.1 Study Selection

The initial search yielded 3,847 records. After removing 892 duplicates, 2,955 records were screened by title and abstract. Of these, 234 full-text articles were assessed for eligibility. Ultimately, 67 studies met all inclusion criteria and were included in the systematic review, with 52 providing sufficient data for meta-analysis.

3.2 Study Characteristics

The 67 included studies encompassed 1,284,592 patients across 23 countries. The majority of studies originated from the United States (n=18, 26.9%), China (n=14, 20.9%), and India (n=8, 11.9%). Study designs included retrospective cohort (n=38, 56.7%), prospective cohort (n=19, 28.4%), and cross-sectional (n=10, 14.9%).

**Table 1. Summary of Included Studies by AI Model Type**

| AI Model Type | Studies (n) | Total Patients | Pooled Sensitivity | Pooled Specificity | AUC |

|---|---|---|---|---|---|

| CNN (Deep Learning) | 28 | 612,340 | 95.8% | 93.2% | 0.967 |

| Random Forest | 12 | 198,450 | 91.4% | 89.7% | 0.924 |

| Support Vector Machine | 9 | 145,230 | 89.2% | 88.1% | 0.891 |

| Recurrent Neural Network | 8 | 178,920 | 93.6% | 91.5% | 0.948 |

| Ensemble Methods | 10 | 149,652 | 94.1% | 92.0% | 0.955 |

3.3 Diagnostic Accuracy

The pooled sensitivity across all AI models was 94.2% (95% CI: 92.1–96.3%, I²=34.2%), and pooled specificity was 91.8% (95% CI: 89.4–94.2%, I²=41.7%). The overall area under the SROC curve was 0.961 (95% CI: 0.945–0.977).

Deep learning models, particularly CNNs, demonstrated significantly higher diagnostic accuracy compared to traditional machine learning algorithms (AUC: 0.967 vs. 0.891, p < 0.001). Among deep learning architectures, ResNet-based models achieved the highest sensitivity (96.4%, 95% CI: 94.2–98.6%), while EfficientNet variants showed the best specificity (94.8%, 95% CI: 92.1–97.5%).

3.4 Time-to-Diagnosis Reduction

Twenty-three studies reported time-to-diagnosis outcomes. AI-assisted workflows reduced the average time-to-diagnosis by 47.3% (95% CI: 38.1–56.5%, p < 0.001). The median time reduction was from 72 hours (IQR: 48–120) with conventional methods to 38 hours (IQR: 18–64) with AI-assisted methods.

3.5 Early-Stage Detection

Eighteen studies compared early-stage CVD detection rates between AI and conventional approaches. AI-assisted diagnosis showed a 23.6% improvement (95% CI: 17.4–29.8%, p < 0.001) in early-stage detection, defined as identification of disease before the onset of clinical symptoms.

4. Discussion

This systematic review and meta-analysis provides comprehensive evidence supporting the diagnostic efficacy of AI-based tools for cardiovascular disease detection. The pooled results demonstrate that AI algorithms achieve high sensitivity (94.2%) and specificity (91.8%), with deep learning models—particularly CNNs—outperforming traditional machine learning approaches.

Several key findings merit discussion. First, the consistently high diagnostic accuracy across diverse study populations suggests that modern AI models possess reasonable generalizability. This is particularly important for clinical translation, as diagnostic tools must perform reliably across different demographic groups, healthcare settings, and geographic regions.

Second, the significant reduction in time-to-diagnosis (47.3%) has profound implications for clinical practice. In acute cardiovascular events such as myocardial infarction, where "time is muscle," faster diagnosis can directly translate to improved patient outcomes (Anderson et al., 2023). AI-assisted triage systems in emergency departments could prioritize high-risk patients, potentially reducing mortality rates.

Third, the improvement in early-stage detection (23.6%) addresses one of the most critical challenges in cardiovascular medicine. By identifying subclinical disease states, AI tools could enable preventive interventions that reduce disease progression and associated healthcare costs (Vasan et al., 2024).

4.1 Limitations

This review has several limitations. First, the majority of included studies were retrospective in design, which may introduce selection bias. Second, heterogeneity in AI model architectures, training procedures, and evaluation protocols makes direct comparisons challenging. Third, most studies were conducted in high-income settings with well-curated datasets, limiting the extrapolation of findings to resource-limited environments. Fourth, the rapid evolution of AI technology means that some included models may already be superseded by newer architectures.

4.2 Future Directions

Future research should prioritize prospective, multicenter clinical trials that evaluate AI diagnostic tools in real-world settings. Standardized benchmarking frameworks, such as those proposed by the FDA's Digital Health Center of Excellence, are essential for ensuring consistent evaluation across studies. Additionally, explainability and interpretability of AI models must be enhanced to build clinician trust and facilitate regulatory approval.

5. Conclusions

This systematic review and meta-analysis demonstrates that AI-based diagnostic tools significantly enhance the early detection of cardiovascular diseases. Deep learning models, particularly CNNs, offer superior sensitivity and specificity compared to both traditional machine learning and conventional diagnostic approaches. The substantial reduction in time-to-diagnosis and improvement in early-stage detection rates highlight the transformative potential of AI in cardiovascular medicine. However, rigorous prospective validation, regulatory standardization, and ethical frameworks must be established before widespread clinical implementation.

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Acknowledgements

The authors thank the librarians at GIMS and UCL for their assistance with the systematic search strategy. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflict of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The datasets analyzed during this study are available from the corresponding author upon reasonable request. The complete PRISMA checklist and search strategy are provided in the supplementary materials.

References

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