Artificial Intelligence in Cardiac Imaging: A Comprehensive Review of Clinical Implementation and Validation

Amita Kajrolkar
Freelance Writer, Mumbai, India
Correspondence to: emmydixit@gmail.com

DOI: https://doi.org/10.70389/PJC.100003

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Amita Kajrolkar – Conceptualization, Writing – original draft, review and editing
• Guarantor: Amita Kajrolkar
• Provenance and peer-review:
  Commissioned and externally peer-reviewed
• Data availability statement: N/a

Keywords: Cardiac imaging, ai model validation, Deep learning architectures, Echocardiography applications, Regulatory
frameworks.

Peer-review
Received: 17 November 2024
Revised: 26 January 2025
Accepted: 28 January 2025
Published: 17 February 2025

Introduction

Cardiovascular diseases account for more than one-third of the global mortality rate, and more than 17.3 million people die from CVDs every year.¹ The increased need for accurate and fast diagnostic imaging in cardiology has resulted in the incorporation of AI technologies ranging from simple image analysis to real-time decision making.² This is because deep learning and other AI architectural designs can enhance diagnostic precision while reducing time-draining tasks.³

Evolution of AI in Cardiac Imaging

The major advances associated with AI in cardiac imaging include shifting from simple algorithm-based techniques to some advanced, data-based models.

Key milestones include:

• 1980s–1990s: Behind the increased use of a rule-based approach in discipline learning, pattern recognition of simple shapes.
• 2000s: Predictive analytics emerged due to the appearance of machine learning.
• 2010s–present: They further reported that by using deep learning, the enhancements of several important aspects occurred, including the automated segmentation, classification, and superior feature extraction in the cardiac imaging instance.⁴

Current Applications in Cardiac Imaging Modalities

Echocardiography

AI has enhanced echocardiography with applications such as:

Automated Chamber Quantification and Functional Assessment: AI models perform current real-time chamber segmentation and estimated ejection fraction at par with a clinical investigator-level accuracy.^5,6 For instance, Zhang et al. have, using the AI, estimated left ventricular ejection fraction with a 95% degree of accuracy.⁷

Valve Morphology and Dysfunction Analysis: These sophisticated integrated systems categorize such conditions as valvular stenosis and valvular regurgitation. One study by Kusunose et al. showed a sensitivity and specificity of AI analysis of valvular abnormalities to be greater than 93%.⁸

Disease-Specific Applications: AI helps to diagnose diseases such as hypertrophic cardiomyopathy and amyloidosis, but the first signs or subtle changes in the echo image could be overlooked during the interpretation of the images.⁹

Throughout 2024 researchers have made big steps forward in creating automatic diagnostic systems. New deep learning tools can now process 3D chamber measurements to produce expert-level results fast (r = 0.98).¹⁰ Deep learning networks now perform automatic valve dynamics evaluations at 45% faster speeds with no decline in diagnostic precision.¹¹ Recent studies have also focused on edge computing solutions to improve AI implementation in echocardiography, making it more efficient in real-time clinical settings.¹²

Cardiac Computed Tomography (CT)

• Coronary Artery Analysis: This means that automated coronary calcium scoring and stenosis are great accomplishments in CT imaging. Other works of Johnson et al. have shown how AI is capable of performing the CCS with maximum efficiency and accuracy by comparing it with the standard manual method with an impressive correlation coefficient of 0.97.
• Perfusion Imaging: AMI-P is the world’s first fully automated software that can detect ischemia using artificial intelligence (AI) myocardial perfusion analysis, which cuts down the need for expensive tests such as angiography.¹³
• Plaque Characterization and Risk Stratification: Current AI systems have the capacity to analyze the morphology and composition of plaque with the view of understanding the vulnerable plaques that are characteristic of acute coronary syndromes (Table 1).13 AI-driven plaque characterization using deep learning is now improving risk assessment and decision-making for coronary artery disease.¹⁴

Table 1: Current AI applications across cardiac imaging modalities.
Imaging Modality	AI Applications	Key Capabilities	Performance Highlights
Echocardiography	Chamber Quantification Valve Morphology Analysis Disease-Specific Diagnosis	Realtime chamber segmentation Ejection fraction estimation Detecting subtle disease changes	95% accuracy in ejection fraction >93% sensitivity in valvular abnormality detection
Cardiac CT	Coronary Artery Analysis Perfusion Imaging Plaque Characterization	Automated calcium scoring Ischemia detection Vulnerable plaque identification	0.97 correlation coefficient in calcium scoring Reduced need for invasive angiography
Cardiac MRI	Automated Segmentation Tissue Characterization Flow and Strain Analysis	Myocardial region demarcation Fibrosis and scar tissue detection Myocardial deformation measurement	Comparable to expert-level evaluation Critical in detecting cardiomyopathies
Nuclear Cardiac Imaging	Perfusion Defect Detection Dose Optimization Motion Correction	Reducing false positives Radiation dose precision Automatic motion detection	Maintained sensitivity levels Enhanced image resolution

Cardiac Magnetic Resonance (CMR)

• Automated Segmentation and Tissue Characterization: AI algorithms are useful for better demarcation of myocardial, ventricular, and atrial regions, as well as for the determination of fibrosis and scar tissue.¹⁵ Martinez et al. have recently tried to confirm that automated analysis of CMR is comparable to the evaluation of CMR done by an expert.¹⁶
• Quantitative Flow and Strain Analysis: Modern computer programs have consequently provided new tools for the measurement of myocardial deformation and perfusion, which are critical in detecting cardiomyopathies and valvular diseases.16 Cloud-based AI solutions for nuclear cardiac imaging have further enhanced workflow efficiency and accessibility across multiple healthcare institutions.¹⁷

Nuclear Cardiac Imaging

• Automated Perfusion Defect Detection: The overlying use of AI eliminates the high false positive that is associated with SPECT imaging without compromising their sensitivity level.¹⁸
• Dose Optimization and Motion Correction: Precision in dosage of radiation, automatic detection of patient motion, and subsequent corrections enhance both image resolution and safeguarding of individuals.¹⁸ AI-based motion correction in cardiac imaging is significantly improving image quality and diagnostic accuracy.¹⁹

Validation Studies and Performance Metrics

Validation studies are relevant for total report quality and total study in terms of facilitating the generalizability of AI applications in cardiac imaging and establishing clinical significance. This section discusses the issues related to performance metrics of using AI, as well as methodological peculiarities of the multi-center trials aimed at confidence in the AI algorithms.

Methodological Considerations

AI has been enhanced by strong validation methods supporting cardiac imaging. Clearly identified key aspects are discussed as dataset diversity, acquisition or construction of ground truth, external tests and validation, and unified evaluation protocols. New validation systems use adaptive learning methods that let models grow better over time while staying steady in performance.²⁰ Researchers routinely combine multiple hospital data sets through federated learning without breaking patient privacy.²¹

Dataset Diversity: Heterogeneous datasets are crucial to train AI models for external validity – across different groups, across multiple types of scans or tests, and across multiple clinical scenarios. If information is collected from homogenous samples, algorithms are likely to perform poorly when tested on low-performing groups.^1,22 AI models trained on such datasets are relatively effective for garden-variety pathology, CAD, for instance, but less so for esoteric pathology like HCM.²³ In recent developments like federated learning, several institutions can work together to enrich the dataset’s variety without violating the privacy of the data.^24,25

Ground Truth Establishment: The ‘ground truth’ is hence important for creating and calibrating models for analysis. Gold standards involving diagnostic procedures like fractional flow reserve for coronary artery disease, consensus readings, or expert annotations give confidence in reference.^3,26 Nonetheless, such inconsistency across the board may impact quality because different experts demonstrate varying levels of proficiency in their annotations.²⁷

External Validation: Models built based on internal data sources perform significantly worse in external populations. Applying AI systems in different clinical settings, and validation using geographically separate datasets or multi-center, assures to get accurate AI performance.^15,28

Standardized Protocols: There is no consensus in validation approaches regarding definitions of performance measures and imaging acquisition protocols, and these hamper the comparison of results acquired from various studies. Current guidelines call for standardized assessment templates to support measures such as sensitivity, specificity, and Dice Similarity Coefficients (DSC), among others^29,30 among the evaluation paradigms.

Performance Metrics

Performance measures, in an analytical way, the capability to diagnose, segment, and operate AI systems. They are essential for checking algorithm results against human expert gold standards and an algorithm’s clinical relevance.

Sensitivity and Specificity: Sensitivity analyses how the True Positive rate is assessed by the algorithm, while on the other hand, specificity measures how the True Negative rate is determined by the algorithm. For example, many studies that use AI-based models in coronary CT angiography show sensitivity and specificity rates are higher than 90% in terms of stenosis detection.^6,13,31

Area Under the Receiver Operating Characteristic Curve (AUC): AUC offers the additional advantage of summing up the differentiation of diagnostic accuracy along a range of thresholds. Superior AI techniques in myocardial perfusion imaging have demonstrated an AUC of greater than 0.95, suggesting an excellent discriminatory power.^22,32

DSC: DSC compares the region of interest, which is segmented in advance, and the region segmented with ground truth, which is often used in tasks such as the measurement of ventricular volume or quantification of myocardial fibrosis. The DSC was >0.85, thereby suggesting a comparable generalization of the automated CMR segmentation to that achieved by Martinez et al.^7,16

Inter-reader and Intra-reader Variability: Utilizing AI in interpretation means that results will always be standardized across clinicians, leading to less variable results. In echocardiography, the deep learning model showed a substantial increase in intra- and inter-reader variability in detecting regional wall motion abnormality.^4,8,33

Time Efficiency and Workflow Impact: In most cases, the use of automated features decreases the amount of time spent on analysis, improving clinical processes. For example, in CT, AI-specific calcium scoring has resulted in a cut time of over 30%, leading to faster reporting.^5,34

Patient-Centered Metrics: The use of real-world framework aspects such as clinical outcomes and cost-effectiveness guarantees algorithms are aligned with valid concerns. For example, AI has been described as leading to lower repeat imaging rates and adverse event rates, which do carry practicability.^18,35

Multi-Center Validation

Multi-center validation studies are used to evaluate AI systems’ performance on different datasets and image acquisition techniques in different patient groups.

Need for Multi-Center Studies

Single-center validation of the models is not effective as it does not factor in equipment used in imaging, acquisition settings used, or referring hospital’s patient population. Such concerns, however, can be minimized by the multi-center nature of the study, as Wang et al. conducted a 15-center validation of the generalizability of cardiac CT AI.^15,36

Design and Methodology

Effective multi-center validation requires:

• Dataset Heterogeneity: The underside of patients from varying areas, using multiple imaging techniques, and with different disease burdens provides broad validation.^22,28
• Uniform Ground Truth Standards: Shared annotations or consensus reads reduce the amount of variation in validation standards because they are centralized.^26,27
• Cross-Center Performance Analysis: The stability of the algorithms is assessed in regard to changes in imaging hardware, for instance, different vendors or scanner generations.³⁷

Challenges in Multi-Center Validation

• Data Integration: It also contains harmonization of imaging formats and imaging protocols due to the variability of these parameters. Such standardized frameworks are provided by DICOM and similar formats that aid in simplifying such a process.^36,38
• Privacy and Security: For instance, GDPR and HIPAA restrict the exchange of data, but in cases like the collaborative validation processes described in federated learning.²⁵
• Logistical and Financial Constraints: Prospective cohort studies are, therefore, labor and time-consuming, especially when they are conducted across different centers.^24,39
• Clinical Impacts of Multi-Center Validation: If this is the case, then multi-center studies bear out the steady performances and give clinicians confidence. The sample also meets regulatory standards that are currently given by the Food and Drug Administration (FDA) or CE to ensure generalisability.^40,41

Implementation Challenges and Solutions

On the application of AI in cardiac imaging, this review finds the following major difficulties that exist at the technical, clinical, and systemic levels, although the use of AI in cardiac imaging is noted to hold great change. Addressing these challenges is important for the successful adoption and implementation of AI benefits in the healthcare environment.

Technical Challenges

Edge computing technology has overcome past implementation requirements through recent advances. Modern tiny AI systems can operate perfectly well on typical medical workstations without demanding specialized hardware purchases.⁴² AI tool interface stability increases through improved software security and cloud hosting services, which smaller healthcare providers now use thanks to this development.⁴³

Data Quality and Standardization

The different imaging methods used, the various formats of images, and even the quality of images obtained from one institution to another act as major barriers to the use of AI. For example, variations in imaging quality caused by differences in scanner manufacturers, variations in disease prevalence across centers, or, most especially, variations in operator experience can affect the algorithm’s efficiency.^1,36 However, heterogeneous datasets are also an issue since it takes the algorithm longer to train. Hence, it will depend on the particularity of that data set.

Solutions: It also emphasizes the use of standard levels of imaging acquisition and data, typically the DICOM, which makes the results more compatible across institutions.^15,25 There are computer algorithms that flag low-quality images to enable quality checks during the process of image acquisition.³⁴ This ensures that the institutions carry out model training collectively without passing the raw data, which is beneficial in increasing dataset variety as well as standardization of data.^24,25

Compatibility with Other Systems

AI, in addition to the already present PACS, EHR, and clinical systems, is not an easy proposition. Challenges include some incompatibilities with other operating systems. Interferences of the workflow during the implementation process. Data security, particularly in connection with governing standards such as GDPR or HIPAA.^26,28 Solutions: Vendor-neutral AI platforms allow for the integration with PACS and EHR systems independent of vendor.^13,35 This strategy minimizes the disruption of workflow since the implementation is done gradually, and staff trained as implementation proceeds.²⁹ Additional safeguards include encryption of data and audit trails to ensure actual compliance with privacy laws, as 38 pointed out.

Clinical Challenges

User Acceptance

With respect to AI-employed cardiac imaging, there are some barriers that inhibit the implementation of this solution by healthcare providers: potential job loss, the reliability of AI outputs, and the opaqueness of the decision-making processes involved. There has been a concern that a large number of clinicians still do not feel comfortable depending on AI systems, especially in emergent diagnosis.^27,30

Solutions: Some publications identify the effectiveness of special training in the form of workshops and exercises as crucial for clinicians to familiarize themselves with AI.^8,33 The expansion of explainable AI (XAI) systems improves the interpretability of the solutions that, in turn, correlate with clinical reasoning with patient attention.^16,24 They concluded that pilot implementations enable clinicians to directly engage with AI and feel compelled to trust its therapeutic worth.³⁶

Clinical Validation

AI systems tend to excel in clearly defined environments and do comparatively poorly in a clinical context, which is the real world containing a different patient population obtained using different imaging methods and containing diverse diseases compared to those used in the training set. For example, the algorithms developed based on data from environments with great resources can perform worse in low-resource or patients with diverse characteristics.^9,10

Solutions: The strict validation for the algorithm on the datasets originating from different geographical regions increases the algorithm’s generality and stability.^26,32 Real-world follow-up evidence shows routine AI use in practice: error reduction, diagnostic improvement, and decrease in redundant imaging,^18,31 and in realistic monitoring as well as feedback enhance algorithm effectiveness in real context.²²

Economic and Organizational Barriers

These challenges include high implementation costs, such as equipment upgrades, personnel retraining, and costs of ongoing maintenance for resource-scarce healthcare facilities. Furthermore, organizational path dependencies, or the resistance to change, can become a cause of AI implementation.

Solutions: Barriers to adoption include the following strategies: financial, where cost-sharing models that include partnerships between public institutions and the Developer AI employed in the project are cleared.³⁷ The return on investment research can provide evidence to shareholders on the benefits of using AI technology.³⁵ Out of the 10 variables studied, organizational readiness for change was most affected by leadership support and clear communication of the benefits of AI.^8,32

Ethical and Legal Considerations

The use of AI in applications leads to various ethical issues in patient data, biased algorithms, and individual responsibility in patient treatment. Thus, unbiased algorithms can harm less-represented demographical categories and increase healthcare inequalities.^1,39 Moreover, the rules regulating the liability for AI mistakes in diagnosis are still not very rigid.⁴¹

Solutions: Different training datasets reduce prejudice and increase equal correctness across populations.²⁵ In the case of FDA and CE guidelines, responsibility is being defined with the help of clear rules and regulations for patient protection.^40,41 Ethical monitoring and the following of privacy rules guarantee appropriate and realistic AI applications.³⁸

Table 2: Summary of challenges and solutions.
Challenge	Solutions
Data Quality and Standardization	Standardized protocols, automated quality checks, federated learning
Integration with Systems	Vendor-neutral platforms, phased implementation, enhanced cybersecurity
User Acceptance	Training programs, XAI, pilot implementations
Clinical Validation	External validation, long-term outcome studies, continuous monitoring
Economic Barriers	Cost-sharing models, ROI analyses, organizational readiness strategies
Ethical and Legal Considerations	Diverse datasets, clear regulatory guidelines, ethical reviews

Through solving these issues by means of correct approaches, AI can be implemented into the system of cardiac imaging and contribute to the enhancement of accuracy of diagnostics, efficiency of work, and, consequently, quality of life of patients.

Future Directions

AI’s role in cardiac imaging as a diagnostical, analytical, and prognostic tool for cardiac diseases and interventions as part of a new human–technology interface has a very promising future in individualized treatment methods. As AI architectures evolve, new methods, applications, techniques, and technologies, the possibility to overcome current flaws and enrich clinical application is promising (Figure 1).

Advanced AI Architectures

• Federated Learning: Several institutions are capable of jointly training AI models through federated learning without the need for transferring original data while, at the same time, enhancing model robustness. In this case, federated learning takes advantage of decentralized data to overcome biases rooted in a single-center dataset and to enhance the solidness of algorithms.^1,2
• XAI: Interpretable AI enhances transparency as it brings to light algorithmic choices that a regular user cannot understand on their own. This creates confidence among clinicians, especially when used to quantify applications such as stenosis in the coronary or determination of tissue type. New developments have shown that XAI, when used, is able to explain the importance of features in both cardiac CT and CMR.^3,4
• Transfer Learning: It minimizes the requirement for highly effective datasets by rewriting the algorithm for different tasks, for example, changing the imaging approaches or distinct diseases. Some research confirms the feasibility of transfer learning in echocardiographic model adaptation to new datasets with the least retraining.^5,6

Novel Applications

• Multimodality Integration: Among others, echocardiography, cardiac CT, CMR, and nuclear imaging give a detailed evaluation of the patient’s heart. It is possible for AI to synergistically work on these datasets to overcome the limitations of risk stratification and come up with personalized treatment. For example, the integration of ‘hybrid’ AI models has been proposed to integrate CMR’s tissue characterization ability with CT’s ability to provide a detailed assessment of coronary anatomy for a comprehensive assessment of the disease.^7,8
• Predictive Analytics: The analytic power of AI is growing not only in terms of diagnosing but also in terms of forecasting the course of the disease and treatment outcomes. The authors were able to train machine learning models to study major adverse cardiac events using imaging as well as clinical information, which helped in the formulation of preventive approaches.⁹
• Personalized Medicine: Genomic, clinical, and imaging data using AI are opening up a new avenue in the future of patient-specific therapeutic intervention. For instance, AI-generated solutions for identifying potential responders to targeted therapies such as CRT are still being researched, improved, and developed.²²

Technical Innovations

• Edge Computing: Considering the fact that in edge computing most of the imaging data is processed on devices, not on servers, the latency is cut. This allows AI interventions during a procedure like intraoperative echocardiography or stress tests.¹³
• Quantum Computing: As new branches of quantum computing are being set, it has the capability to power the huge computational requirements for future AI models, especially in the genetic analysis of multimodal images and massive simulations.²⁶
• Enhanced Visualization Techniques: Technological advancements are in the form of AI-facilitated visualization including a virtual tour of the inside of the heart that is assisting in diagnosis and procedures. These tools have shown feasibility in pre-procedure assessments and intra-procedure applications.¹⁵

Emerging Areas of Research

• AI in Underserved Settings: The delay in the deployment of AI solutions in low-resource settings could be checked with carefully designed solutions that meet those environment’s needs. Low-resolution imaging AI models or those designed to function on portable Ultrasound have been deemed to enhance reach-out cards.^16,44
• AI-Driven Clinical Trials: Its application in clinical research includes; Patient enrollment in clinical trials, design of trial endpoints, and real-time monitoring of trial results. This approach has the advantage of shortening the trial length and enhancing the quality of the generated data.¹⁸
• Dynamic AI Models: Hence, there is growing research on creating dynamic models that are capable of updating their knowledge with the newly incoming data in real time (so-called online learning) to fit the changing clinical practice. These systems contribute to the improvement of algorithm output over time while at the same time ensuring that the output is this makes the systems generalizable in their nature.⁴⁵

Challenges and Future Prospects

AI is still an innovative and fairly promising technology, but problems like algorithm prejudgment, possible legal restrictions, and lack of proper support in terms of infrastructure are still actual. Removing these barriers can only be done by integration, rigorous validation, and adherence to regulatory frameworks. With more innovations being developed, it is believed that AI will revolutionize cardiac imaging through faster, more accurate, and patient-specific interventions.^11,29

Regulatory and Economic Considerations: AI incorporation into cardiac imaging leads to legal issues and challenges in the economic sense. These factors are crucial in order to achieve safe, effective, and equitable utilization in the clinical areas (Figure 2).

Regulatory Frameworks

Licensing criteria for AI tools in cardiac imaging focus on safety, effectiveness, and explainability paradigms. Government agencies of various nations, including the FDA of the United States, the European Medicines Agency of the European Union, and other global authorities, are responsible for making such frameworks. The regulatory approaches of interest for the current study are as follows:

FDA Approval Process: The FDA has established two primary pathways for AI approval:

• Premarket Notification (510k): For tools believed to be about parity with currently available technologies.
• De Novo Classification: For the new AI devices, which have no precedent, for creating clinical validation and risk analysis.¹ For instance, an FDA-approved AI-based left ventricular function assessment tool was tested to establish non-inferiority with the expert cardiologists.²
• CE Marking in the European Union: CE marking requires adhering to EU MDR, which stresses performance assurance, safety, and post-market monitoring.³ Recent changes include details to AI tools where details concerning algorithm explain Ability and risk management were included.⁴
• International Standards: Frameworks to harmonize AI regulations have been proposed by the International Medical Device Regulators Forum and the encouragement of homogeneity in validation, labeling, and monitoring practices.⁵

Challenges in Regulatory Approval

• Dynamic Nature of AI: Unlike most software where its programs can remain frozen for a long time without changes, an AI model may update or learn as it functions. Challenges exist about how these unique regulatory pathways accredit and approve these species of “adaptive AI” systems without compromising patient safety.⁶
• Explainability and Trust: For applications such as the detection of coronary artery disease, regulatory agencies demand intelligent tools to give understandable results. The first five reasons are as follows:6 The need to have a clinician understanding of the AI decision making;⁷ It empowers clinicians to validate the dependability of the AI decision.
• Bias and Equity: It investigates an AI algorithm’s fairness by asking whether it was accurate across different subgroups, to reduce injury to vulnerable patient populations that result from algorithm precision on biometric invariant features.⁸

Clinical Implementation Guidelines: Major professional associations and governing bodies have started developing or releasing guidelines for AI in the clinical setting.

• Society Guidelines: Of importance, the ACC and ESC give guidelines for the application of AI in cardiovascular imaging. These guidelines emphasize the need for: Heavier validation studies conducted before these treatments are actually taken to be used in clinical practice. Sustained performance monitoring after using a particular system. Approaches to integration that do not disturb the organizational workflow are discussed.^9,22
• Quality Assurance Measures: AI tools must be audited periodically hence can only prove that they are still in compliance with the set performance and safety standards. Real-time monitoring systems are known to have been suggested in cases of identification of parameters of algorithmic drift arising from new input data.¹³

Economic Considerations: That is why the economic efficiency of AI usage in cardiac imaging may be described in terms of business interest and payoff, including costliness, ROI, and effectiveness of usage, as well as influence on such key production factors as factor intensity.

• Cost-Effectiveness Analysis: Cost-effective analysis refers to an approach of comparing the usefulness between two options with a limited budget available for use in the course of carrying out the research. AI tools have shown potential to reduce healthcare costs by:
• Minimizing Repeat Imaging: AI enhances the accuracy of diagnosis, thereby less likely to order other tests. For instance, concerning MPAs, automated myocardial perfusion analysis has been directly correlated with 20% lower rates of repeat imaging studies.¹⁵
• Optimizing Workflow Efficiency: While tasks like chamber quantification become very time-consuming for human operators, AI cuts down the time spent on reporting. A large trial of seven radiologists carried out in multiple centers found increased productivity of radiologists by about 30% when using the AI-based cardiac CT.¹⁶
• Reducing Diagnostic Errors: Myocardial infarction, for example, must be identified and treated early to minimize costly ramifications and subsequent hospital admissions.⁴⁴
• Reimbursement Models: Still, the absence or the lack of best practices and a clear line of reimbursing AI tools could be an issue.

Key considerations include:

• Inclusion in Payor Schemes: Joint work is being done to seek reimbursement for insurance claims, and Medicare itself has introduced NTAP for AI diagnostics.¹⁸
• Value-Based Payment Models: Therefore, concepts such as efficiency and better results with less costs fit the VBC models that reward providers based on value rather than volume.⁴⁵
• First-Time Investment and Distribution of Resources: Thus, there are many barriers to AI such as initial costs associated with procuring hardware and training the software and personnel to run it. Thus, the proper application of these technologies remains a major challenge in the adoption of smaller hospitals and even in resource-limited settings. These difficulties can be reduced through partnerships between public organizations and private sector companies.¹¹

Ethical and Legal Considerations

• Accountability for Errors: Moral and legal blurs are still defining the distribution of blame when an AI-facilitated diagnostic mistake occurs. Frameworks have to be clear about whether or not the liability rests with the developer of the AI or the healthcare provider.²⁹
• Data Privacy and Security: It is important to adhere to rules and regulations such as HIPAA and GDPR when implementing AI in clinical settings. Measures for addressing privacy concerns include data minimization, data anonymization, and encryption in particular.⁴⁶
• Equity in Access: Healthcare organizations are unequally positioned within the market, and economic imbalances that exist can be exacerbated through disparities in access to AI tools, which then decreases the quality of care provided to unique populations. As it stands, policymakers must ensure that the uses of AI are spread fairly across the population.⁴⁷

Conclusion

Terms and conditions to regulate and control the usage of AI in cardiac imaging have to be implemented in consideration of the economic factors for a successful implementation. More transparent rules, alongside the signs of cost efficiencies and equal access, will play a crucial role in realizing the potential of AI to revolutionize cardiac care. Further work by the developers, regulators, clinicians, policymakers, and other stakeholders will define the integration of AI in health systems given the progressive growth of health technology.

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Cite this article as:
Kajrolkar A. Artificial Intelligence in Cardiac Imaging: A Comprehensive Review of Clinical Implementation and Validation. Premier Journal of Cardiology 2025;2:100003

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