Artificial intelligence is playing an increasingly prominent role in cardiac imaging, particularly in computed tomography (CT) and magnetic resonance imaging (MRI). Despite its promise, the technology remains at varying stages of development, with significant challenges impeding widespread clinical implementation. Recognising the transformative impact of AI in this field, several leading organisations—including the European Society of Cardiovascular Radiology (ESCR), the Society for Imaging Informatics in Medicine (SIIM) and the Radiological Society of North America (RSNA)—have issued a scientific statement on the current status, challenges and future direction of AI applications in cardiac CT and MRI.
Optimising Workflow Efficiency in Cardiac Imaging
One of AI’s most immediate benefits in cardiac CT and MRI is improving workflow efficiency, particularly in patient selection, scheduling and imaging protocol optimisation. Given the growing demand for cardiac imaging, it is crucial to ensure that the right test is chosen for each patient while reducing unnecessary scans. AI-driven decision support systems can analyse patient records, including structured data such as diagnosis codes and unstructured clinical notes, to guide physicians in selecting the most appropriate imaging modality. By integrating AI into this process, clinicians can reduce the likelihood of low-value or redundant imaging, improving resource allocation and patient outcomes.
Scheduling optimisation is another key area where AI can have a significant impact. Predictive models can analyse historical data to forecast peak imaging demand and identify patients at higher risk of missing appointments. This allows healthcare facilities to adjust schedules dynamically, reducing waiting times and improving overall efficiency. Moreover, AI can facilitate automated protocol selection based on patient-specific factors such as medical history and biometric data. This ensures that each scan is optimised for diagnostic accuracy while minimising radiation exposure and contrast agent use, enhancing patient safety and comfort.
Despite these advantages, the adoption of AI-driven workflow optimisation tools remains limited, with most applications still in the early developmental stages. Further research and validation are needed to ensure these systems can be integrated seamlessly into diverse healthcare environments.
Enhancing Image Analysis and Diagnostic Interpretation
AI is also revolutionising image acquisition, reconstruction and interpretation in cardiac CT and MRI. In CT imaging, AI can assist in patient positioning, ensuring precise alignment to reduce motion artefacts and optimise scan quality. Automated positioning systems have demonstrated improved accuracy compared to manual methods, leading to lower radiation doses and higher-quality images. Similarly, AI-based reconstruction techniques enable the generation of high-resolution images from lower-dose scans, mitigating noise while preserving diagnostic detail.
In coronary CT angiography, AI has shown promise in automating the detection and quantification of coronary artery plaque. By leveraging machine learning algorithms, AI can assist radiologists in assessing the degree of luminal stenosis, classifying coronary artery disease severity, and identifying high-risk plaque features. These capabilities enhance risk stratification and support clinical decision-making by providing more objective, reproducible assessments of coronary artery disease.
Similarly, AI-driven segmentation tools in cardiac MRI enable automated extraction of key clinical parameters such as ventricular volumes, myocardial function and tissue characterisation. These automated techniques have demonstrated high levels of accuracy and efficiency, significantly reducing the time required for manual post-processing. AI is also being explored for its potential to improve myocardial strain analysis and late gadolinium enhancement quantification, both of which are essential for evaluating conditions such as cardiomyopathy and myocardial infarction. However, while AI tools for functional cardiac assessment are becoming more widely available, applications for advanced myocardial tissue characterisation remain in earlier stages of development.
Despite these advancements, AI-based image analysis faces several challenges, including the need for extensive validation across different imaging systems and patient populations. Current evidence is often derived from single-centre studies with limited sample sizes, raising concerns about generalisability. Large, diverse datasets and external validation studies are crucial to ensuring that AI tools perform reliably across various clinical settings.
Addressing Ethical and Regulatory Considerations
The deployment of AI in cardiac imaging raises important ethical and regulatory questions, particularly concerning data bias, transparency and patient safety. AI models are only as reliable as the data on which they are trained, and biases in training datasets can lead to disparities in model performance across different demographic groups. Regulatory bodies such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have introduced guidelines to ensure AI-driven medical devices meet rigorous safety and efficacy standards. However, ongoing monitoring is necessary to evaluate AI systems in real-world settings and address potential biases that may arise.
In addition to ethical concerns, the environmental impact of AI is an emerging issue. AI-driven imaging requires substantial computational resources, contributing to increased energy consumption and carbon emissions. Strategies such as optimising data storage, using energy-efficient computing methods and minimising redundant AI model training can help mitigate these concerns.
Successful AI deployment also depends on regulatory clarity, interdisciplinary collaboration and ongoing model refinement. Healthcare providers, data scientists and policymakers must work together to ensure that AI solutions are not only clinically effective but also equitable, sustainable and aligned with patient-centred care.
AI is expected to transform cardiac CT and MRI by enhancing workflow efficiency, improving diagnostic accuracy and optimising patient outcomes. However, despite its rapid development, most AI tools remain in the early to mid-stages of clinical implementation. Significant challenges—including regulatory hurdles, ethical considerations and the need for comprehensive validation—must be addressed before AI can achieve widespread adoption in cardiac imaging.
The scientific statement issued by ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM and RSNA underscores the importance of a balanced approach to AI integration. While AI offers immense potential, its deployment must be guided by rigorous evaluation, regulatory oversight and a commitment to patient safety. A multidisciplinary approach involving clinicians, data scientists and policymakers will be essential to ensuring that AI-driven cardiac imaging delivers tangible benefits without compromising healthcare equity or quality.
Source: Radiology
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