Artificial Intelligence In Cardiology Research Articles

Artificial intelligence in cardiology: Hope for the future and power for the present.

Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With the pressures for improved care and translation of the latest medical advances and knowledge to an actionable plan, clinical decision-making for cardiologists is challenging. Artificial Intelligence (AI) is a field in computer science that studies the design of intelligent agents which take the best feasible action in a situation. It incorporates the use of computational algorithms which simulate and perform tasks that traditionally require human intelligence such as problem solving and learning. Whilst medicine is arguably the last to apply AI in its everyday routine, cardiology is at the forefront of AI revolution in the medical field. The development of AI methods for accurate prediction of CVD outcomes, non-invasive diagnosis of coronary artery disease (CAD), detection of malignant arrythmias through wearables, and diagnosis, treatment strategies and prediction of outcomes for heart failure (HF) patients, demonstrates the potential of AI in future cardiology. With the advancements of AI, Internet of Things (IoT) and the promotion of precision medicine, the future of cardiology will be heavily based on these innovative digital technologies. Despite this, ethical dilemmas regarding the implementation of AI technologies in real-world are still unaddressed.

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 in Cardiology—A Narrative Review of Current Status

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.

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

Following Bernard Gersh MD’s feature in the EHJ Dialogues series, he and his colleague Paul Friedman MD offered to give CardioPulse readers a closer look into the future of artificial intelligence (AI) in cardiology. Drs Gersh and Friedman are widely respected cardiologists at Mayo Clinic in Rochester, MN, USA. Dr Friedman is a pioneer who helped develop software to allow doctors and researchers to derive data and information using AI from electrocardiograms (ECGs). Dr Gersh has studied the integration of AI into clinical practice. Both agree that we have only just begun to discover the potential of AI in medicine. In medicine, AI refers to the process by which algorithms are used to derive knowledge from data. Dr Gersh likens AI to the modern car: ‘I don’t understand what goes on in there, but I know how to use it’. In cardiology, the most well-researched use of...

Artificial intelligence in cardiology: fundamentals and applications.

Artificial intelligence (AI) is an overarching term that encompasses a set of computational approaches that are trained through generalised learning to autonomously execute specific tasks. AI is a rapidly expanding field in medicine. In particular cardiology, with its high reliance on numerical patient data in decision making, has great potential to benefit from AI. Types of AI, including neural networks and computer vision, can dramatically change the day-to-day workflow of cardiologists, primarily through integration in diagnostic imaging modalities, periprocedural planning, electronic health record analysis and patient monitoring. Healthcare systems will undoubtedly become more automated and shift to more AI-driven methods to improve efficiency and reduce cost. Patients in the end will benefit from these changes with improved diagnostic accuracy, better tailored treatments resulting in a greater quality and quantity of life. In this article, we will describe some of the fundamental principles underlying AI that physicians should have an understanding of, along with current clinical applications.

The Emergence of Artificial Intelligence in Cardiology: Current and Future Applications.

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.

Trends in cardiology and oncology artificial intelligence publications

Study objectiveTo determine whether there has been growth in publications on the use of artificial intelligence in cardiology and oncology, we assessed historical trends in publications related to artificial intelligence applications in cardiology and oncology, which are the two fields studying the leading causes of death worldwide. Upward trends in publications may indicate increasing interest in the use of artificial intelligence in these crucial fields. Design/settingTo evaluate evidence of increasing publications on the use of artificial intelligence in cardiology and oncology, historical trends in related publications on PubMed (the biomedical repository most frequently used by clinicians and scientists in these fields) were reviewed. ResultsFindings indicated that research output related to artificial intelligence (and its subcategories) generally increased over time, particularly in the last five years. With some initial degree of vacillation in publication trends, a slight qualitative inflection was noted in approximately 2015, in general publications and especially for oncology and cardiology, with subsequent consistent exponential growth. Publications predominantly focused on “machine learning” (n = 20,301), which contributed to the majority of the accelerated growth in the field, compared to “artificial intelligence” (n = 4535), “natural language processing” (n = 2608), and “deep learning” (n = 4459). ConclusionTrends in the general biomedical literature and particularly in cardiology and oncology indicated exponential growth over time. Further exponential growth is expected in future years, as awareness and cross-disciplinary collaboration and education increase. Publications specifically on machine learning will likely continue to lead the way.

Novel Artificial Intelligence Applications in Cardiology: Current Landscape, Limitations, and the Road to Real-World Applications.

Cardiovascular diseases are the leading cause of death globally and contribute significantly to the cost of healthcare. Artificial intelligence (AI) is poised to reshape cardiology. Using supervised and unsupervised learning, the two main branches of AI, several applications have been developed in recent years to improve risk prediction, allow large-scale analysis of medical data, and phenotype patients for personalized medicine. In this review, we examine the key advances in AI in cardiology and its limitations regarding bias in the data, standardization in reporting, data access, and model trust and accountability in cases of error. Finally, we discuss implementation methods to unleash AI's potential in making healthcare more accurate and efficient. Several steps need to be followed and challenges overcome in order to successfully integrate AI in clinical practice and ensure its longevity.

Cardiovascular Events Prediction using Artificial Intelligence Models and Heart Rate Variability

Artificial Intelligence is exponentially evolving into a solution to many of humanity's complex problems. In this context, healthcare is benefiting from this technology and all its branches to improve the level of services offered, including cardiac health services. Cardiovascular diseases have always been among the most common and deadly diseases around the world, as studies have consistently shown. However, Artificial Intelligence services offer several tools to improve the diagnosis of these diseases and even predict their occurrence. In this study, four models are created and trained with ”PhsyioNet Smart Health for Assessing the Risk of Events via ECG Database” to analyze the characteristics of heart rate variability and predict the occurrence of heart diseases and cerebrovascular events. The results obtained support the confidence in the use of Artificial Intelligence in cardiology, where Support Vector Machines, Deep Neural Networks, and XGBoost achieved an accuracy of 91.80%, 90.19%, and 89.10%, respectively.

Artificial intelligence in cardiology: the debate continues.

In 1955, when John McCarthy and his colleagues proposed their first study of artificial intelligence, they suggested that 'every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it'. Whether that might ever be possible would depend on how we define intelligence, but what is indisputable is that new methods are needed to analyse and interpret the copious information provided by digital medical images, genomic databases, and biobanks. Technological advances have enabled applications of artificial intelligence (AI) including machine learning (ML) to be implemented into clinical practice, and their related scientific literature is exploding. Advocates argue enthusiastically that AI will transform many aspects of clinical cardiovascular medicine, while sceptics stress the importance of caution and the need for more evidence. This report summarizes the main opposing arguments that were presented in a debate at the 2021 Congress of the European Society of Cardiology. Artificial intelligence is an advanced analytical technique that should be considered when conventional statistical methods are insufficient, but testing a hypothesis or solving a clinical problem-not finding another application for AI-remains the most important objective. Artificial intelligence and ML methods should be transparent and interpretable, if they are to be approved by regulators and trusted to provide support for clinical decisions. Physicians need to understand AI methods and collaborate with engineers. Few applications have yet been shown to have a positive impact on clinical outcomes, so investment in research is essential.

Artificial intelligence in cardiology: the next big thing?

Artificial intelligence in cardiology: the next big thing?

Clinical applications of artificial intelligence in cardiology on the verge of the decade.

Artificial intelligence (AI) has been hailed as the fourth industrial revolution and its influence on people's lives is increasing. The research on AI applications in medicine is progressing rapidly. This revolution shows promise for more precise diagnoses, streamlined workflows, increased accessibility to healthcare services and new insights into ever-growing population-wide datasets. While some applications have already found their way into contemporary patient care, we are still in the early days of the AI-era in medicine. Despite the popularity of these new technologies, many practitioners lack an understanding of AI methods, their benefits, and pitfalls. This review aims to provide information about the general concepts of machine learning (ML) with special focus on the applications of such techniques in cardiovascular medicine. It also sets out the current trends in research related to medical applications of AI. Along with new possibilities, new threats arise - acknowledging and understanding them is as important as understanding the ML methodology itself. Therefore, attention is also paid to the current opinions and guidelines regarding the validation and safety of AI-powered tools.

Artificial intelligence in cardiology : Relevance, current applications, and future developments

Big data and applications of artificial intelligence (AI), such as machine learning or deep learning, will enrich healthcare in the future and become increasingly important. Among other things, they have the potential to avoid unnecessary examinations as well as diagnostic and therapeutic errors. They could enable improved, early and accelerated decision-making. In the article, the authors provide an overview of current AI-based applications in cardiology. The examples describe innovative solutions for risk assessment, diagnosis and therapy support up to patient self-management. Big data and AI serve as abasis for efficient, predictive, preventive and personalised medicine. However, the examples also show that research is needed to further develop the solutions for the benefit of the patient and the medical profession, to demonstrate the effectiveness and benefits in health care and to establish legal and ethical standards.

Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic

Background and aimsArtificial Intelligence (AI) shows extensive capabilities to impact different healthcare areas during the COVID-19 pandemic positively. This paper tries to assess the capabilities of AI in the field of cardiology during the COVID-19 pandemic. This technology is useful to provide advanced technology-based treatment in cardiology as it can help analyse and measure the functioning of the human heart. MethodsWe have studied a good number of research papers on Artificial Intelligence on cardiology during the COVID-19 pandemic to identify its significant benefits, applications, and future scope. AI uses artificial neuronal networks (ANN) to predict. In cardiology, it is used to predict the survival of a COVID-19 patient from heart failure. ResultsAI involves complex algorithms for predicting somewhat successful diagnosis and treatments. This technology uses different techniques, such as cognitive computing, deep learning, and machine learning. It is incorporated to make a decision and resolve complex challenges. It can focus on a large number of diseases, their causes, interactions, and prevention during the COVID-19 pandemic. This paper introduces AI-based care and studies its need in the field of cardiology. Finally, eleven major applications of AI in cardiology during the COVID-19 pandemic are identified and discussed. ConclusionsCardiovascular diseases are one of the major causes of death in human beings, and it is increasing for the last few years. Cardiology patients' treatment is expensive, so this technology is introduced to provide a new pathway and visualise cardiac anomalies. AI is used to identify novel drug therapies and improve the efficiency of a physician. It is precise to predict the outcome of the COVID-19 patient from cardiac-based algorithms. Artificial Intelligence is becoming a popular feature of various engineering and healthcare sectors, is thought for providing a sustainable treatment platform. During the COVID-19 pandemic, this technology digitally controls some processes of treatments.

Technical and practical aspects of artificial intelligence in cardiology.

Artificial intelligence (AI) is here to stay. It is not a future anymore, and there are many particular problems in cardiology that are already being solved via machine learning (ML), and many more are to come. AI cannot solve complex tasks yet, and probably this will not change in the upcoming years. Therefore, cardiologists do not have to be afraid that computers will replace them. However, cardiologists who will not be able to use ML algorithms in their clinical practice will be replaced by those who will. (Fig. 2, Ref. 50). Keywords: artificial intelligence, cardiology, potential machine learning, survival models, classification algorithms, computer vision, automated analysis of various imaging examinations, ECG interpretation, phenotype clustering, pathophysiological mechanisms.

Artificial intelligence in cardiology

In the first wave of artificial intelligence (AI), rule-based expert systems were developed, with modest success, to help generalists who lacked expertise in a specific domain. The second wave of AI, originally called artificial neural networks but now described as machine learning, began to have an impact with multilayer networks in the 1980s. Deep learning, which enables automated feature discovery, has enjoyed spectacular success in several medical disciplines, including cardiology, from automated image analysis to the identification of the electrocardiographic signature of atrial fibrillation during sinus rhythm. Machine learning is now embedded within the NHS Long-Term Plan in England, but its widespread adoption may be limited by the “black-box” nature of deep neural networks.

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

There is currently no other hot topic like the ability of current technology to develop capabilities similar to those of human beings, even in medicine. This ability to simulate the processes of human intelligence with computer systems is known as artificial intelligence (AI). This article aims to clarify the various terms that still sound foreign to us, such as AI, machine learning (ML), deep learning (DL), and big data. It also provides an in-depth description of the concept of AI and its types; the learning techniques and technology used by ML; cardiac imaging analysis with DL; and the contribution of this technological revolution to classical statistics, as well as its current limitations, legal aspects, and initial applications in cardiology. To do this, we conducted a detailed PubMed search on the evolution of original contributions on AI to the various areas of application in cardiology in the last 5 years and identified 673 research articles. We provide 19 detailed examples from distinct areas of cardiology that, by using AI, have shown diagnostic and therapeutic improvements, and which will aid understanding of ML and DL methodology.

Artificial Intelligence in Cardiology: Concepts, Tools and Challenges - "The Horse is the One Who Runs, You Must Be the Jockey".

The recent advances at hardware level and the increasing requirement of personalization of care associated with the urgent needs of value creation for the patients has helped Artificial Intelligence (AI) to promote a significant paradigm shift in the most diverse areas of medical knowledge, particularly in Cardiology, for its ability to support decision-making and improve diagnostic and prognostic performance. In this context, the present work does a non-systematic review of the main papers published on AI in Cardiology, focusing on its main applications, potential impacts and challenges.

Machine Meets Biology: a Primer on Artificial Intelligence in Cardiology and Cardiac Imaging.

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.

Artificial intelligence in cardiology: applications, benefits and challenges

Evidently, the potential benefits of AI in cardiology are enormous. However, such benefits are not without challenges. First, there are clear benefits for improving work productivity. There are currently fewer physicians to care for an ever - increasing aging population (WHO 2016). AI can support, rather than replace physicians, generating time - and cost - saving benefits for them and their patients and enabling more compassionate and thorough interactions. However, as more tasks become automated, there are possibilities that fewer physicians will be required to work or that fewer will do so on a full - time basis, since many tasks could be delivered through platforms by part-time, freelancer physicians. This may impact the relationship between patients, physicians and administrative staff in healthcare systems.