Artificial intelligence in cardiology

Artificial intelligence (AI) is an integral part of clinical decision support systems (CDSS), offering methods to approximate human reasoning and computationally infer decisions. Such methods are generally based on medical knowledge, either directly encoded with rules or automatically extracted from medical data using machine learning (ML). ML techniques, such as Artificial Neural Networks (ANNs) and support vector machines (SVMs), are based on mathematical models with parameters that can be optimally tuned using appropriate algorithms. The ever-increasing computational capacity of today’s computer systems enables more complex ML systems with millions of parameters, bringing AI closer to human intelligence. With this objective, the term deep learning (DL) has been introduced to characterize ML based on deep ANN (DNN) architectures with multiple layers of artificial neurons. Despite all of these promises, the impact of AI in current clinical practice is still limited. However, this could change shortly, as the significantly increased papers in AI, machine learning and deep learning in cardiology show. We highlight the significant achievements of recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take a central stage in the field.

Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases.

Trends in cardiology and oncology artificial intelligence publications

Bernard Gersh and Paul Friedman on the future of artificial intelligence in cardiology.

Artificial intelligence (AI) in cardiology represents a great opportunity, if further developed in a trustworthy way, to support human intelligence in daily practice. AI could help cardiologists to operate with greater efficacy and efficiency, supporting precision, timeliness, ethics, while meeting all patients' needs. AI, however, is not yet so widely diffused in cardiology and important challenges and obstacles have to be overcome, concerning ethics, conflict of interests, algorithm improvements and transparency, product certification, input processing, cyber security, privacy, and need for collaboration and cooperation of different involved professions, within and between different institutions of heterogeneous complexity.

Artificial intelligence technology is emerging as a promising entity in cardiovascular medicine, potentially improving diagnosis and patient care. In this article, we review the literature on artificial intelligence and its utility in cardiology. We provide a detailed description of concepts of artificial intelligence tools like machine learning, deep learning, and cognitive computing. This review discusses the current evidence, application, prospects, and limitations of artificial intelligence in cardiology.

Background — This comprehensive literature review aims to examine key milestones in the development of artificial intelligence (AI) in cardiology, highlight current advances, and identify significant challenges associated with AI integration that are underrepresented in the literature. Using a rigorous methodological approach, this review provides a detailed understanding of the transformative potential and practical limitations of AI technologies in cardiovascular medicine. Methods — A thorough search of e-Library, PubMed, and Scopus databases was conducted using a carefully selected set of keywords, including ‘artificial intelligence’, ‘expert systems’, ‘personalized medicine’, ‘polygenic risk assessment’, ‘neural networks’, ‘large language models’, ‘deep learning’, ‘cardiovascular disease’, and ‘prevention’. The search encompassed publications from 1995 to June 2025, resulting in a total of 268 articles that were systematically reviewed and analyzed. A subset of 65 articles was subsequently selected for in-depth review to ensure a comprehensive and representative sample of the current state of research in this field. Results — This article provides a comprehensive analysis of the AI application in cardiology, tracing the historical evolution of technological advances, current achievements, existing challenges, and prospects for integration. The development of AI in cardiology is described in detail from the development of the first expert systems to the emergence of sophisticated neural network models that have demonstrated exceptional accuracy in the diagnosis and prognosis of cardiovascular diseases (CVD). Particular attention is paid to the use of AI for cardiovascular risk prediction, including the integration of polygenic risk scores (PRS) with patient clinical data. The main drivers of AI adoption are revealed, including a chronic shortage of medical specialists, economic demands, and rapid technological innovation. Additionally, the article identifies significant barriers, such as patient and healthcare professional resistance, legal and ethical considerations, data quality issues, and cybersecurity challenges. Conclusion — It is emphasized that modern AI systems are shifting from human-dependent learning paradigms to autonomous self-learning models, which facilitates their accelerated development and integration into clinical practice. In conclusion, it should be noted that the successful and safe implementation of AI in cardiology depends on the readiness of the medical community, patients, and healthcare systems to engage in collaborative transformation, as well as on the improvement of the regulatory framework and quality control mechanisms for AI technologies.

Applications of Artificial Intelligence in Cardiology. The Future is Already Here.

Artificial Intelligence in Cardiology: AnAustralian Perspective.

Artificial Intelligence (AI) essentially refers to various types of machine learning, often involving deep neural networks. It autocompletes our ideas as we write, enables us to communicate with our phones, and supports language translation.1 According to the 2019 Global Burden of Disease Study, the estimated age-standardized incidence of cardiovascular disease (CVD) in Pakistan was 918.8 per 100,000 people (global: 684.33 per 100,000), and the age-standardized death rate was 357.88 per 100,000 (global: 239.85 per 100,000).2 With AI, new analytical and data-driven approaches could lead to significant advances in understanding multimorbid groups of cardiology patients and potentially improve therapeutic strategies.3 AI has been used to interpret echocardiograms and heart rhythms from ECGs, and to detect indicators of heart disease, such as left ventricular dysfunction, from surface ECGs and nuclear cardiology.4-6 It is a misconception that AI will replace cardiologists. Instead, skilled practitioners will be able to expand their clinical capabilities, make more accurate and prompt diagnoses, and improve management decisions in patient care. As with any statistical application, it is important to understand AI's strengths and limitations. To understand the basics of AI, it starts with developing an algorithm based on human expertise. Programmers create relationships between input and output, known as expert systems. In machine learning, a general algorithm, such as a neural network, approximates a mathematical relationship between input data and expected outputs. In unsupervised learning, such as clustering, only the inputs are fed into the algorithm, which then finds insights in the data using its inner structure and statistics. An AI model can discover new relationships in data that have previously eluded human discovery.1 For research purposes, cardiologists using AI may follow these steps: Type and collection of data. Preprocessing of data. Choosing the right machine learning approach. Validating and evaluating methods and results.3 The application of AI techniques in the healthcare system is still in its infancy and requires more understanding through workshops and integrated learning.7 In conclusion, AI represents a new development in the field of medicine, especially cardiology. However, it is susceptible to significant errors in interpretation and raises safety and ethical concerns. References Lopez-Jimenez F, Attia Z, Arruda-Olson AM, Carter R, Chareonthaitawee P, Jouni H, et al. Artificial Intelligence in Cardiology: Present and Future. Mayo Clin Proc. 2020;95(5):1015-39. Samad Z, Hanif B. Cardiovascular Diseases in Pakistan: Imagining a Postpandemic, Postconflict Future. Circulation. 2023;147(17):1261-3. Gill SK, Karwath A, Uh HW, Cardoso VR, Gu Z, Barsky A, et al. Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare. Eur Heart J. 2023;44(9):713-25. Cheng LT, Zheng J, Savova GK, Erickson BJ. Discerning tumor status from unstructured MRI reports--completeness of information in existing reports and utility of automated natural language processing. J Digit Imaging. 2010; 23(2):119-32. Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol. 2016;68(21):2287-95. Garcia EV, Klein JL, Taylor AT. Clinical decision support systems in myocardial perfusion imaging. J Nucl Cardiol. 2014;21(3):427-39. Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation frameworks for artificial intelligence translation into health care practice: scoping review. J Med Internet Res 2022;24:e32215.

CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?

An understanding of the basics concepts of deep learning can be helpful in not only understanding the potential applications of this technique but also in critically reviewing literature in which neural networks are utilized for analysis and modeling. The term "deep learning" has been applied to a subset of machine learning that utilizes a "neural network" and is often used interchangeably with "artificial intelligence." It has been increasingly utilized in healthcare for computational "learning", especially for pattern recognition for diagnostic imaging. Another promising application is the potential for these neural networks to improve the accuracy in the identification of patients who are at risk for cardiovascular events and could benefit most from preventive treatment in comparison with more conventional statistical techniques. The importance of such tailored cardiovascular risk assessment and disease management in individual patients is far reaching given that cardiovascular disease is the leading cause of morbidity and mortality in the world. Nearly half of myocardial infarctions and strokes occur in patients who are not predicted to be at risk for cardiovascular events by current guideline-based approaches. Equally important are individuals who are not at risk for cardiovascular events and yet are given expensive and unnecessary preventive treatment with potential untoward side effects. The application of powerful artificial intelligence/deep learning tools in medicine is likely to result in more effective and efficient health care delivery with the potential for significant cost savings by shifting preventative treatment from inappropriate to appropriate patient subgroups.

In this article, artificial intelligence (AI) usage and its benefits in medicine are reviewed in the oncology, radiology, and cardiology fields. The relevant literature was searched in PubMed and Google Scholar using the words "Artificial Intelligence", "Artificial Intelligence in Medicine", "Artificial Intelligence in Cardiology", "Artificial Intelligence in Oncology", and "Artificial Intelligence in Radiology" for the last 10 years. This article covers the AI's current implications in daily practice, discussing its advantages and disadvantages based on the findings. AI's effect in medicine for reducing workload, diagnosis, time management, and drug dosing is going to be reviewed especially in radiology, oncology, and cardiology fields as well as general usage of AI in addition to important highlights. Lastly, this minireview evaluates the current challenges of AI technology in medicine and how clinicians should work with this emerging technology.

Unveiling facilitators and barriers to artificial intelligence implementation in cardiac healthcare: Rationale and design of the INSIGHT-AI France study, from the Artificial Intelligence Working Group and the National College of Cardiologists in Training of the French Society of Cardiology.

Artificial intelligence: Friend or foe?