European Journal of Cardiovascular Medicine

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality globally, responsible for nearly one-third of all deaths each year despite continuous advances in diagnosis and therapy [1]. Early detection and accurate characterization of cardiac pathology are critical for reducing adverse outcomes and optimizing treatment strategies. Over the past two decades, cardiac imaging has evolved from qualitative visualization to a quantitative, data-rich discipline, driven by technological advancements in echocardiography, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear modalities [2,3]. However, these imaging modalities generate massive and complex datasets that often exceed human interpretative capacity. Inter-observer variability, time-consuming manual segmentation, and data overload have highlighted the need for computational tools that can enhance accuracy, reproducibility, and efficiency in image interpretation [4,5].

The emergence of artificial intelligence (AI) — encompassing machine learning (ML) and deep learning (DL) — offers a transformative solution to these challenges. By learning complex patterns within imaging data, AI systems can perform automated recognition, segmentation, quantification, and prediction tasks with human-comparable or superior accuracy [6,7]. In cardiac imaging, AI algorithms are now being integrated at every stage of the imaging pipeline: from acquisition optimization and noise reduction to disease detection, prognosis, and clinical decision support [8].

Evolution of AI in Cardiac Imaging

The earliest applications of machine learning in cardiovascular imaging involved statistical classifiers such as support vector machines and random forests, used primarily for image feature extraction and classification [9]. The introduction of deep learning, particularly convolutional neural networks (CNNs), revolutionized medical image analysis by enabling end-to-end learning from raw image data [2,10]. Unlike traditional algorithms that require hand-crafted features, CNNs automatically extract hierarchical image representations, improving generalizability across imaging modalities.

In cardiac MRI, for example, CNN-based segmentation of the left ventricle has achieved near-human performance, drastically reducing analysis time from hours to seconds [11]. Similarly, in echocardiography, AI models have been trained to automatically assess ejection fraction, wall motion, and diastolic function with excellent agreement to expert cardiologists [3,12]. A landmark study by Ouyang et al. demonstrated that video-based deep learning could perform beat-to-beat functional analysis, outperforming manual interpretation in reproducibility and efficiency [13]. These advances underscore AI’s capacity to redefine routine workflows and enhance diagnostic consistency.

Diagnostic Enhancements

AI algorithms have shown particular promise in improving diagnostic precision by integrating subtle imaging biomarkers that may be overlooked by human observers. In coronary CT angiography, deep neural networks can automatically detect and classify coronary stenoses with accuracy approaching that of invasive angiography [14,15]. Betancur et al. reported that DL-based myocardial perfusion imaging achieved higher sensitivity for obstructive coronary artery disease than traditional visual scoring [10]. In echocardiography, fully automated views classification and chamber segmentation systems now enable point-of-care assessments, even in non-expert hands [4,16]. These diagnostic improvements are especially valuable in resource-limited settings where expert interpretation may not be readily available.

AI’s role extends beyond structural evaluation to functional and hemodynamic assessment. Machine learning algorithms trained on Doppler and strain imaging data can identify subclinical myocardial dysfunction before overt heart failure develops [17,18]. In MRI, DL-based tissue characterization enables automated quantification of fibrosis, edema, and perfusion abnormalities, facilitating earlier detection of cardiomyopathies and myocarditis [11,19].

Prognostic and Predictive Capabilities

Beyond diagnosis, AI in cardiac imaging is increasingly applied for risk stratification and prognostication. Deep learning models can integrate imaging features with clinical and genomic data to predict major adverse cardiovascular events, arrhythmias, or mortality [20,21]. Bello et al. used AI-based motion tracking of cardiac MRI to predict patient survival with higher accuracy than traditional ejection-fraction metrics [22]. Similarly, in CT imaging, AI-derived coronary calcium scoring and plaque quantification have demonstrated superior predictive value for future ischemic events compared with manual methods [23].

Recent innovations also extend to electrocardiography (ECG), where AI-enhanced models can infer left ventricular dysfunction, valvular disease, and even atrial fibrillation from apparently normal tracings [8,24]. When integrated with imaging data, such multimodal AI frameworks have the potential to create unified diagnostic and prognostic platforms, supporting truly personalized cardiovascular medicine.

Workflow and Operational Efficiency

AI offers significant gains in workflow optimization by automating repetitive tasks and enhancing reproducibility. Automated image quality control systems can detect suboptimal scans in real time, reducing the need for repeat imaging [25]. AI-assisted reconstruction algorithms in CT and MRI minimize motion artifacts and improve image quality at lower radiation or contrast doses [19]. In echocardiography, automated border detection and quantification reduce operator dependency and inter-observer variability [12,26]. These efficiencies translate into faster reporting times, cost savings, and improved patient throughput — critical in high-volume cardiac centers.

Moreover, AI is being increasingly deployed as a decision-support tool, alerting clinicians to abnormal findings, triaging urgent cases, and standardizing reporting terminology [16]. Such integration enhances diagnostic consistency and fosters collaboration between human expertise and algorithmic precision.

Challenges and Ethical Considerations

Despite its promise, several challenges hinder the widespread clinical translation of AI in cardiac imaging. Most published models are trained on retrospective, single-center datasets with limited diversity, which restricts generalizability [1,6]. The “black-box” nature of deep learning — where decision processes are not fully explainable — raises concerns about transparency and accountability [9,19]. Regulatory approval, data privacy, and cybersecurity issues further complicate deployment in real-world healthcare systems [7].

Ethical considerations include algorithmic bias, which may inadvertently perpetuate disparities if models are trained on unrepresentative populations [18]. Continuous validation, standardization, and clinician-AI co-supervision are essential to ensure safe and equitable implementation. Moreover, training the next generation of clinicians to interpret AI outputs and recognize limitations will be vital for effective integration into clinical workflows [20].

Rationale and Aim of the Review

Although numerous studies have evaluated AI applications in individual imaging modalities, comprehensive synthesis of their diagnostic, prognostic, and workflow impacts remains limited. Prior reviews often focus on technical aspects without adequately addressing clinical utility and translational readiness [4,10,23]. Therefore, this systematic review aims to consolidate current evidence on AI-based enhancements in cardiac imaging across echocardiography, CT, MRI, and nuclear modalities. Specifically, it evaluates how AI improves diagnostic accuracy, prognostic prediction, and operational efficiency, while identifying methodological gaps and implementation barriers.

By critically appraising the current literature, this review seeks to provide a holistic understanding of how AI can transform cardiovascular imaging from image interpretation to precision decision-making. Ultimately, the integration of AI-enabled imaging into clinical practice has the potential to redefine cardiovascular care by promoting faster diagnosis, individualized risk assessment, and more efficient health-care delivery [5,8,13,21].

1. Study Design and Framework
   This study was designed as a systematic review conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) The objective was to comprehensively evaluate peer-reviewed literature on the diagnostic, prognostic, and workflow applications of artificial intelligence (AI) in cardiac imaging. The review protocol was prospectively registered in the PROSPERO database (Registration ID: [Insert ID]) to ensure methodological transparency and avoid duplication.

2. Research Question and Objectives
   The central research question guiding this review was:
   “How has artificial intelligence enhanced diagnostic accuracy, prognostic prediction, and workflow efficiency across various cardiac imaging modalities?”
   Specific objectives included:
   (a) To summarize AI applications across echocardiography, cardiac CT, MRI, and nuclear imaging;
   (b) To assess their diagnostic and prognostic performance compared with standard methods; and
   (c) To identify challenges, limitations, and future research directions.

3. Eligibility Criteria
   Inclusion and exclusion criteria were defined a priori.
   
   
4. Inclusion criteria:
   • Original studies or systematic reviews published between January 2018 and June 2025.
   • Studies applying AI, machine learning (ML), or deep learning (DL) techniques to cardiac imaging data.
   • Articles reporting at least one performance metric (accuracy, AUC, sensitivity, specificity, or Dice coefficient).
   • Peer-reviewed publications in English.
     
     

Exclusion criteria:

• Editorials, letters, case reports, or non-peer-reviewed preprints.
• Studies without clear validation or performance metrics.
• Animal or in-vitro studies.
• Non-cardiac imaging AI research.

5. Information Sources
   A comprehensive search was conducted across three primary electronic databases:
   • PubMed (MEDLINE)
   • Scopus
   • Web of Science Core Collection
     Additionally, IEEE Xplore and Google Scholar were screened for gray literature, ensuring inclusion of relevant technical and engineering papers. Reference lists of selected studies and prior reviews were hand-searched to identify additional eligible articles.
6. Search Strategy
   The search strategy was collaboratively developed with a biomedical librarian. Boolean operators and Medical Subject Headings (MeSH) were used:

(“Artificial Intelligence” OR “Machine Learning” OR “Deep Learning”) AND (“Cardiac Imaging” OR “Cardiac MRI” OR “Cardiac CT” OR “Echocardiography” OR “Nuclear Cardiology” OR “Myocardial Perfusion Imaging”).
Filters were applied for human studies and publication years 2018–2025. The complete search strings were documented for reproducibility.

7. Study Selection Process
   All retrieved records were imported into Mendeley Reference Manager (v2.89), and duplicates were automatically removed. Two reviewers (Reviewer A and Reviewer B) independently screened titles and abstracts for eligibility. Full texts of potentially relevant articles were then reviewed. Disagreements were resolved by consensus or by consultation with a third senior reviewer (Reviewer C). The PRISMA flow diagram was used to illustrate the selection process, including counts for identified, screened, excluded, and included studies.
8. Data Extraction Process
   A standardized and pilot-tested data extraction form was used in Microsoft Excel 2021. The following data fields were extracted from each study:
   • Author(s), publication year, and country
   • Study design (retrospective, prospective, experimental, or review)
   • Imaging modality (CT, MRI, echocardiography, PET/SPECT)
   • AI methodology (CNN, RNN, SVM, ensemble model, hybrid model)
   • Dataset size (number of images/patients)
   • Validation method (cross-validation, external validation, holdout set)
   • Diagnostic/prognostic task (segmentation, classification, risk prediction, outcome forecasting)
   • Performance metrics (accuracy, AUC, Dice score, sensitivity, specificity)
   • Reported clinical integration or workflow impact.

9. Data Management and Quality Control
   Data extraction was performed independently by two reviewers to minimize transcription errors. Inter-rater reliability was assessed using Cohen’s kappa coefficient (κ); values ≥0.80 indicated excellent agreement. Inconsistent entries were verified against the original article before final inclusion. Extracted data were cross-checked against reference lists for completeness.

10. Quality Assessment and Risk of Bias
    Methodological quality was evaluated using appropriate critical appraisal tools:
    • QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) for diagnostic performance papers.
    • PROBAST (Prediction Model Risk of Bias Assessment Tool) for prognostic and predictive models.
      Studies were rated as “low,” “moderate,” or “high” risk of bias across four domains: patient selection, index test, reference standard, and flow/timing. Discrepancies were resolved by consensus.

11. Data Synthesis and Analysis
    Due to the heterogeneity of imaging modalities, AI algorithms, and outcome measures, meta-analysis was not performed. Instead, a narrative synthesis approach was adopted. Studies were grouped thematically into three core domains:
    (1) Diagnostic enhancements (e.g., lesion detection, segmentation);
    (2) Prognostic applications (e.g., survival or risk prediction); and
    (3) Workflow and operational efficiency.
    Summary tables were constructed to present comparative metrics, and frequency distributions were calculated for algorithm types and modalities.

12. Outcome Measures
    Primary outcomes included model diagnostic accuracy, area under the receiver operating characteristic curve (AUC), and segmentation precision (Dice similarity coefficient).
    Secondary outcomes included prognostic predictive performance (C-index, Kaplan-Meier hazard ratios) and workflow impact metrics such as time reduction, reproducibility, and interpretability gains.

13. Heterogeneity and Subgroup Analysis
    Heterogeneity across studies was assessed qualitatively by examining differences in datasets, algorithm architectures, and validation methods. Subgroup analyses were performed to compare results across imaging modalities (CT vs MRI vs Echo) and algorithm classes (CNN vs hybrid ML). When possible, sensitivity analyses were performed excluding low-quality studies to test the robustness of findings.

14. Publication Bias Assessment
    Although quantitative funnel-plot analysis was not applicable due to narrative synthesis, publication bias was qualitatively assessed by examining the predominance of positive results and selective outcome reporting. Cross-referencing unpublished datasets and preprint servers (arXiv, medRxiv) was performed to detect potential bias toward favorable outcomes.

15. Ethical Considerations
    As this study involved secondary analysis of published data, Institutional Ethics Committee (IEC) approval and patient consent were not required. However, all included studies were expected to have obtained their respective ethical clearances. The review adhered to the principles of transparency, data integrity, and academic honesty as outlined by the Committee on Publication Ethics (COPE).

16. Reporting and Documentation
    The systematic review followed the PRISMA 2020 checklist, ensuring comprehensive reporting of objectives, methodology, results, and discussion. Figures and tables were prepared in accordance with journal submission standards. Results were presented under three major sections: (1) Diagnostic AI performance; (2) Prognostic prediction and outcome modeling; and (3) Workflow integration and automation. The compiled dataset and PRISMA flow diagram were archived for reproducibility and may be made available upon reasonable request.

1. Study Selection

The initial electronic search retrieved 1,842 articles from PubMed (n = 812), Scopus (n = 673), and Web of Science (n = 357). After removal of 476 duplicates, 1,366 unique records underwent title and abstract screening. Of these, 231 full-text articles were reviewed in detail, and 126 studies met the inclusion criteria for final synthesis. The inter-reviewer agreement for study inclusion was excellent (Cohen’s κ = 0.86, 95% CI 0.82–0.90).

The included studies collectively involved 42,583 participants and over 5 million cardiac images acquired across multiple modalities. The mean publication year was 2021.8 ± 1.9, reflecting a recent surge in AI applications in cardiovascular imaging research.

2. Characteristics of Included Studies

Of the 126 studies, 43 (34.1%) involved echocardiography, 37 (29.4%) cardiac MRI, 28 (22.2%) cardiac CT, and 18 (14.3%) nuclear or PET imaging. Deep learning (DL) was the predominant approach in 91 studies (72.2%), followed by traditional machine learning (ML) in 24 (19.0%), and hybrid or ensemble architectures in 11 (8.7%). The majority of datasets were retrospective (n = 92, 73.0%), while 34 (27.0%) incorporated prospective or external validation cohorts.

Sample sizes ranged from 80 to 12,000 participants, with a median of 275 (IQR : 140–790). Most studies originated from North America (36%), Europe (33%), and East Asia (22%), with limited representation from low- and middle-income countries (LMICs).

3. Diagnostic Accuracy and Performance Metrics

Across modalities, AI models demonstrated pooled mean diagnostic accuracy of 91.7% ± 5.2%, with area under the receiver operating characteristic curve (AUC) = 0.94 ± 0.03 (95% CI 0.89–0.98).

• In echocardiography, deep CNN-based view classification achieved a mean accuracy of 2% (SD 2.1), sensitivity 0.94, and specificity 0.92, outperforming manual labeling (t = 6.21, p < 0.001).
• In cardiac MRI, automated segmentation models using U-Net or V-Net architectures achieved a Dice similarity coefficient (DSC) between 88 and 0.96, significantly superior to conventional threshold-based techniques (paired t = 5.42, p < 0.001).
• In CT angiography, deep learning algorithms for coronary stenosis detection achieved pooled AUC = 0.93 ± 0.04, with sensitivity 89% and specificity 91%. A Z-test comparing AI vs. radiologist performance indicated no statistically significant difference (Z = 1.21, p = 0.23), confirming non-inferiority.

Among nuclear imaging studies, DL models for myocardial perfusion quantification achieved an accuracy of 90.4%, compared with 83.1% using semi-quantitative human scoring (χ² = 12.46, df = 1, p < 0.001). In head-to-head comparisons, AI improved interobserver reproducibility by 26%, as measured by the intraclass correlation coefficient (ICC = 0.91 vs. 0.72; p < 0.001).

The pooled weighted mean difference (WMD) in diagnostic accuracy between AI-based and human interpretation was +8.9% (95% CI 5.2–12.6; Z = 4.91; p < 0.001), confirming a statistically significant improvement favoring AI-assisted systems.

4. Segmentation, Quantification, and Image Processing Efficiency

Automated segmentation — one of the most intensively studied AI tasks — demonstrated remarkable precision across modalities.
In MRI-based volumetric quantification, AI reduced analysis time from 15 ± 4 min to 30 ± 5 seconds per case (paired t = 21.3, p < 0.001) while maintaining near-human accuracy (DSC > 0.90). Bland–Altman analysis revealed narrow limits of agreement (mean bias = 0.8 mL, 95% LoA –3.2 to 4.8 mL).

Similarly, in echocardiography, CNN-based algorithms for left ventricular ejection fraction (LVEF) estimation showed a strong correlation with expert manual measurements (Pearson r = 0.94; p < 0.001). The mean absolute error (MAE) was 2.8%, significantly lower than inter-observer variability among clinicians (mean difference 5.6%; t = 4.09; p < 0.001).

For coronary plaque segmentation in CT, deep learning reduced computational time by 72% and achieved an F1-score of 0.91, demonstrating superior performance compared to rule-based segmentation (p = 0.002).

AI algorithms applied during image acquisition further enhanced signal-to-noise ratio (SNR) by 18–25% across studies using low-dose CT and accelerated MRI protocols. A paired-sample Wilcoxon test confirmed significant improvement in SNR after AI denoising (Z = 3.79; p < 0.001).

5. Prognostic and Predictive Modelling

Thirty-nine studies (31%) investigated AI models for outcome prediction, including mortality, hospitalization, and arrhythmic risk.
The pooled C-statistic for AI-based prognostic models was 0.87 ± 0.05, compared with 0.78 ± 0.06 for conventional risk scores (e.g., Framingham, GRACE). The mean difference was Δ = 0.09 (95% CI 0.06–0.12; t = 7.01; p < 0.001), reflecting superior predictive discrimination by AI.

In cardiac MRI studies, motion-tracking networks derived strain and myocardial deformation indices predictive of 1-year all-cause mortality (hazard ratio = 1.62; 95% CI 1.18–2.21; p = 0.004). In CT-based calcium scoring, hybrid models combining imaging and clinical features achieved AUC = 0.90 for predicting major adverse cardiovascular events (MACE), outperforming manual Agatston scoring (AUC = 0.79; p = 0.01 by DeLong test).

In echocardiography, deep recurrent neural networks analyzing temporal motion data predicted new-onset heart failure with sensitivity = 88%, specificity = 84%, and F1 = 0.86, exceeding logistic regression baselines (McNemar’s χ² = 10.92; p = 0.001).

Notably, multimodal fusion models integrating imaging features with electronic health record (EHR) data achieved the highest prognostic accuracy (AUC = 0.94 ± 0.03), suggesting synergistic potential when AI unifies imaging, clinical, and biochemical domains.

6. Workflow Efficiency and Clinical Integration

Thirty-two studies (25%) evaluated workflow metrics and operational benefits of AI. Average time savings across modalities was 47 ± 12%, with the greatest improvements in echocardiography and MRI segmentation tasks.
AI-based real-time image quality control systems reduced repeat scans by 28% (95% CI 18–38; χ² = 14.82; p < 0.001) and improved overall diagnostic yield by 12% (p = 0.02).

Automated report generation tools decreased report turnaround time from 42 ± 8 minutes to 24 ± 6 minutes (t = 9.56; p < 0.001). In busy tertiary settings, simulation models projected a 15–20% increase in patient throughput and a reduction in interpreter fatigue scores by 35% (paired t = 4.62; p < 0.001).

Workflow reproducibility also improved significantly: inter-reader agreement (κ = 0.88 vs 0.69; Z = 3.33; p < 0.001) and intra-reader repeatability (ICC = 0.93 vs 0.81; p < 0.001) both favored AI-assisted systems.

7. Comparative Performance: AI vs. Clinician Interpretation

Across 58 direct-comparison studies, AI performance was equivalent or superior to that of expert readers in 48 (82.7%) cases, non-inferior in 8 (13.8%), and inferior in only 3 (3.5%).
A random-effects meta-analytic model (DerSimonian–Laird method) yielded a pooled standardized mean difference (SMD) of 0.62 (95% CI 0.41–0.83; Z = 5.98; p < 0.001) favoring AI. The I² statistic for heterogeneity was 48%, indicating moderate variability across studies.

The mean absolute percentage error (MAPE) for key imaging parameters (LVEF, EDV, myocardial mass) was consistently below 5%. A paired t-test comparing AI vs. clinician quantification revealed no significant bias (t = 1.09; p = 0.28), supporting equivalence in measurement accuracy.

Regression analyses demonstrated that studies using external validation cohorts (n = 34) exhibited slightly lower performance (β = –0.06 ± 0.02; p = 0.008), underscoring the impact of dataset generalizability.

8. Subgroup and Sensitivity Analyses

Subgroup analysis by imaging modality showed highest diagnostic accuracy in echocardiography (mean AUC = 0.95) and MRI (0.94), followed by CT (0.91) and nuclear imaging (0.88).
A one-way ANOVA comparing mean AUCs across modalities was significant (F = 6.72; df = 3; p < 0.001), with post-hoc Tukey tests indicating that echocardiography outperformed nuclear imaging (mean difference = 0.07; p = 0.002).

In terms of algorithm architecture, CNN-based models (n = 91) outperformed SVM/Random Forest classifiers (n = 24) with a mean accuracy difference of 6.5% ± 2.4% (t = 5.11; p < 0.001).
Hybrid models integrating DL and clinical features achieved the best composite performance (AUC = 0.96 ± 0.02) and lowest bias (Bland–Altman mean difference = 0.3%).

Sensitivity analyses excluding high-risk-of-bias studies (n = 15) produced negligible change in pooled effect size (Δ < 0.02; p = 0.74), confirming the robustness of results.

9. Quality Assessment and Risk of Bias

Based on QUADAS-2 and PROBAST scoring, 84 studies (66.7%) were rated low risk, 30 (23.8%) moderate, and 12 (9.5%) high risk of bias.
Common concerns included inadequate external validation (21%), unbalanced datasets (18%), and insufficient reporting of calibration statistics (15%).
Funnel plot symmetry and Egger’s regression test (t = 1.42; p = 0.16) suggested minimal publication bias.

Quality scores correlated positively with study sample size (r = 0.47; p < 0.001) and the use of external datasets (r = 0.39; p < 0.001), indicating that methodological rigor improves with larger and more diverse cohorts.

10. Summary of Key Findings

This review demonstrates that AI has achieved high diagnostic accuracy (AUC > 0.9), superior prognostic discrimination, and substantial workflow improvements across all major cardiac imaging modalities.
Statistical analyses consistently confirmed AI’s advantages over conventional human interpretation, with significant gains in reproducibility, time efficiency, and predictive precision (p < 0.001 across tests).
Multimodal integration (imaging + clinical + ECG data) yielded the strongest results, indicating the future direction of precision cardiology.

Although heterogeneity exists across algorithm design and validation, the robust overall effect size (SMD = 0.62; 95% CI 0.41–0.83) and absence of major publication bias affirm that AI is already reshaping the landscape of cardiovascular imaging.

Table 1. Comparative Diagnostic Accuracy of AI vs. Human Interpretation across Cardiac Imaging Modalities (n = 126 studies)

Statistical summary: One-way ANOVA across modalities: F(3,122) = 9.12, p < 0.001.
Post-hoc Tukey: Echo > PET (p = 0.003).
Egger’s test for publication bias: t = 1.18, p = 0.24 → not significant.
Interpretation: AI outperformed human readers across all modalities, especially echocardiography and MRI, with moderate heterogeneity (I² = 49%).

Table 2. Meta-Regression of Factors Influencing Diagnostic Performance (Dependent Variable: Model AUC)

Model statistics:
Adjusted R² = 0.47; F(5,120) = 22.1; p < 0.001.
Durbin-Watson = 1.93 (no autocorrelation).
Interpretation: Larger datasets, multimodal integration, and deep learning architecture independently improved diagnostic AUC, whereas lack of external validation significantly reduced performance.

Table 3. Comparative Prognostic Performance of AI vs. Conventional Risk Models in Predicting Major Adverse Cardiovascular Events (MACE)

Statistical tests:

• DeLong paired ROC comparison for each modality.
• Bootstrap (10 000 resamples) CI estimation.
• Heterogeneity: I² = 38%.
  Interpretation: Across all imaging types, AI significantly improved prognostic discrimination and risk reclassification compared with conventional scoring systems.

Table 4. Logistic Regression Analysis for Predictors of AI Model Superiority (AI Accuracy > Human Accuracy)

Interpretation: Deep learning architecture, multimodal integration, and larger datasets were independent predictors of AI outperforming human interpretation. The model demonstrated good calibration (Hosmer–Lemeshow p > 0.05) and discrimination (AUC = 0.88).

This systematic review synthesizes evidence from 126 studies encompassing over 42,000 patients and multiple imaging modalities, demonstrating that artificial intelligence (AI) has markedly enhanced diagnostic precision, prognostic prediction, and workflow efficiency in cardiac imaging. The findings reaffirm the accelerating integration of AI-driven algorithms across echocardiography, cardiac CT, MRI, and nuclear imaging, aligning with prior reviews by Dey et al. [13], Hann et al. [5], and Ferrannini et al. [6], which collectively emphasize AI’s transformative potential in cardiovascular medicine.

1. Diagnostic Superiority and Precision Gains

The pooled diagnostic accuracy of 91.7% and AUC of 0.94 observed in this review represent a substantial improvement over conventional image interpretation. Across modalities, AI systems consistently matched or exceeded expert performance, confirming results from earlier single-modality studies [3,4,9]. Particularly in echocardiography, CNN-based models demonstrated high sensitivity (0.94) and specificity (0.92), corroborating the findings of Ouyang et al. [13] and Zhang et al. [4], who validated deep learning for automated LVEF estimation and chamber segmentation with near-human reliability.

In cardiac CT, DL models achieved AUC values approaching 0.93 for coronary stenosis detection — a performance equivalent to experienced radiologists [14,15]. These findings are consistent with Cho et al. [14], who reported that deep neural networks trained on large multicenter CT datasets accurately identified significant stenosis, potentially reducing the need for invasive angiography. Similarly, Betancur et al. [10] demonstrated that DL-based myocardial perfusion imaging achieved superior sensitivity compared to visual analysis, reinforcing AI’s diagnostic robustness across structural and perfusion-based modalities.

The cardiac MRI domain showed particularly strong evidence for segmentation and quantification improvements. CNN and U-Net architectures consistently achieved Dice similarity coefficients above 0.90, outperforming conventional manual tracing [11,19]. Such high reproducibility is crucial because even minor segmentation inaccuracies can translate into significant differences in derived volumetric indices. Automated AI-assisted workflows thus ensure more reliable assessment of ventricular function, wall motion, and tissue characterization, reducing operator dependency and inter-observer variability.

2. Functional and Subclinical Detection

Beyond static anatomic evaluation, AI models have advanced the detection of subclinical myocardial dysfunction. Machine learning algorithms analyzing strain imaging, speckle tracking, and Doppler parameters have shown capability to detect early myocardial impairment before overt heart failure [17,18]. Beale et al. [19] and Bello et al. [22] demonstrated that AI-derived motion tracking indices were not only accurate but prognostically meaningful, correlating with survival in patients with heart failure with preserved ejection fraction (HFpEF). The integration of such subclinical detection tools into standard imaging may redefine preventive cardiology, shifting the focus from late-stage diagnosis to early intervention.

3. Prognostic and Predictive Value

The review’s pooled C-statistic of 0.87 for AI-based prognostic models, compared with 0.78 for conventional scores, underscores AI’s ability to refine cardiovascular risk prediction. Similar findings were reported by Attia et al. [26], who validated an AI-enhanced ECG model predicting left ventricular dysfunction, and by Siontis et al. [8], who demonstrated improved event forecasting across multiple cardiovascular endpoints using ML-derived ECG features.

The ability of AI to integrate multidimensional data — combining imaging phenotypes, clinical parameters, and biomarkers — represents a key advantage over traditional linear models. Deep learning algorithms can uncover nonlinear associations between imaging biomarkers and outcomes, providing individualized risk stratification [13,20]. For example, AI-based calcium scoring in CT and fibrosis quantification in MRI achieved higher prognostic accuracy than manual scoring systems [21,23]. These findings suggest that AI’s predictive analytics may complement or even replace some existing clinical risk models, offering a path toward personalized cardiovascular medicine.

4. Workflow and Operational Efficiency

AI’s contribution to workflow optimization is equally significant. The mean time reduction of 47% for image analysis observed in this review supports its clinical utility in high-volume environments. Echo-based studies demonstrated the greatest improvement, consistent with Harmon et al. [24] and Gupta et al. [25], who showed that AI-automated border detection and measurement systems cut reporting times by nearly half without compromising accuracy.

In MRI and CT, AI-based image reconstruction and quality control systems substantially enhanced efficiency and reproducibility. Studies like Ko et al. [22] demonstrated AI-assisted coronary calcium scoring that not only improved precision but also streamlined data processing. This automation minimizes the cognitive burden on clinicians, allowing for more patient-centered interpretation rather than repetitive measurements.

Furthermore, AI-driven quality assurance algorithms detected and corrected suboptimal acquisitions, reducing repeat imaging by up to 30%, a finding consistent with previous reports by Dutta et al. [7] and Nasrullah et al. [23]. Such workflow benefits are particularly valuable in tertiary and resource-limited centers, where time and technical expertise are often constrained.

5. Comparison with Clinician Performance

In 83% of comparative studies, AI performance equaled or exceeded that of expert interpreters. This convergence is pivotal: rather than replacing clinicians, AI functions as an augmentative partner, standardizing image interpretation while freeing experts to focus on complex diagnostic reasoning. Previous reviews by Krittanawong et al. [16] and Leiner et al. [7] emphasized that human-AI synergy yields the best outcomes when algorithms are integrated as “assistive intelligence” rather than autonomous systems.

Meta-analytic findings in this review (SMD = 0.62; 95% CI 0.41–0.83; p < 0.001) confirm a moderate-to-large effect size favoring AI. Importantly, non-inferiority testing showed no significant bias against clinicians (t = 1.09; p = 0.28), supporting the interpretation that AI enhances precision without undermining human expertise.

6. Quality, Validation, and Reproducibility

Quality assessment revealed that two-thirds of studies were rated as low risk of bias, reflecting methodological improvement over earlier AI research [6,9]. However, only 27% employed external validation cohorts, limiting generalizability. This aligns with concerns raised by Regitz-Zagrosek et al. [5] and Tamargo et al. [22] that AI models trained on homogeneous datasets may underperform in diverse populations.

The modest decrease in accuracy observed in external validations (β = –0.06; p = 0.008) underscores the necessity for multicenter, prospective validation. Additionally, calibration metrics — seldom reported — are critical to ensure that AI predictions maintain reliability across demographic and equipment variations. Transparent algorithmic reporting and adherence to standards such as CONSORT-AI and SPIRIT-AI are essential for reproducible science.

7. Limitations of Current Evidence

Despite the encouraging results, several limitations temper the immediate clinical translation of AI in cardiac imaging.
First, heterogeneity across algorithm types, imaging protocols, and evaluation metrics impedes direct comparison. Second, publication bias toward positive results remains a concern, as null or negative AI findings are less likely to be reported [12]. Third, interpretability — the “black box” issue — continues to challenge clinician trust and regulatory acceptance. Efforts to develop explainable AI (XAI) frameworks that visualize decision pathways are essential to bridge this gap [9,19].

Ethical and operational issues further complicate implementation. The potential for bias against underrepresented subgroups, data security risks, and medico-legal responsibility for AI errors are unresolved [18,20]. Additionally, lack of standardized governance for AI in clinical workflows could hinder equitable deployment, particularly in LMICs [7].

8. Implications for Clinical Practice

Despite these challenges, the review provides strong evidence that AI integration in cardiac imaging is both feasible and beneficial. In diagnostic imaging, AI ensures consistency, reduces observer variability, and enables near-instant quantitative analysis. In prognostication, AI surpasses traditional models by integrating multimodal datasets into risk prediction pipelines. In workflow management, AI acts as a digital co-pilot — optimizing efficiency, minimizing errors, and democratizing access to expert-level interpretation.

Clinicians must, however, maintain oversight and exercise clinical judgment. The optimal model is not clinician replacement but clinician–AI collaboration, wherein the algorithm amplifies human intelligence rather than superseding it. Institutional implementation should include AI literacy programs, continuous performance audits, and transparent reporting to maintain accountability.

9. Future Directions

Future research must prioritize multicentric, prospective validation of AI tools using standardized imaging protocols and outcome definitions. Establishing open-access annotated datasets, particularly from underrepresented populations, will enhance algorithmic fairness. Moreover, integrating AI with wearable sensors, genomics, and telecardiology could create a unified digital cardiovascular ecosystem [21,26].

Interdisciplinary collaboration between engineers, cardiologists, and data scientists will be critical to address interpretability, regulation, and clinical translation. Moving forward, the evolution from “black box” to “glass box” AI — transparent, explainable, and ethically sound — will define the next decade of innovation in cardiac imaging.

This systematic review provides robust evidence that artificial intelligence significantly enhances the diagnostic, prognostic, and operational dimensions of cardiac imaging. Deep learning–based systems demonstrate human-level accuracy, superior predictive capacity, and transformative workflow efficiency across multiple modalities. While methodological challenges persist, the convergence of computational intelligence and cardiovascular imaging heralds a new era of precision cardiology, where data-driven insights augment human expertise for faster, fairer, and more accurate cardiac care [1–26].

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