Synthetic artificial intelligence in cardiology: from generative models to clinical applications.

Objective: The objective of this narrative review was to summarize and critically appraise current clinical applications of artificial intelligence (AI) in cardiology, with particular emphasis on arrhythmia detection and cardiovascular imaging modalities. Methods: A selective narrative review of peer-reviewed literature published between 2015 and 2025 was performed using PubMed, Scopus, and Web of Science. Studies were selected based on clinical relevance, validation methodology, and reported diagnostic or prognostic performance in human populations. Results: AI-based algorithms demonstrate diagnostic performance comparable to expert interpretation in atrial fibrillation (AF) detection as well as across echocardiography, coronary computed tomography angiography (CCTA), cardiac magnetic resonance imaging (CMR), and intravascular optical coherence tomography (OCT). Beyond diagnostic accuracy, AI has been shown to reduce analysis time and interobserver variability in controlled and retrospective study settings. However, the majority of available studies are retrospective, rely on curated datasets, and lack prospective validation demonstrating a direct impact on clinical outcomes. Conclusions: AI constitutes a valuable decision-support tool in contemporary cardiology, enhancing diagnostic efficiency and risk stratification across multiple imaging and electrophysiological modalities. Nevertheless, broader clinical adoption will require rigorous external validation, improved model interpretability, and prospective outcome-driven studies. This review uniquely integrates evidence across electrophysiology and multiple imaging modalities to identify shared translational challenges and near-term clinical opportunities for AI in cardiology.

Artificial intelligence (AI) is a nontechnical, popular term that refers to machine learning (ML) of various types, but most commonly deep neural networks.The field of cardiology is at the forefront of AI in medicine.Using search terms related to AI and cardiology, we searched PubMed and MEDLINE databases without date restriction.Relevance was considered when selecting articles.In this article, we highlight the major advances in cardiology in recent years and demonstrate how AI is poised to take center stage in the future.To identify the most relevant problems to solve with AI, computer scientists, clinical investigators, clinicians, and other users must collaborate closely.When generating and implementing AI, best practices include selecting ideal data sources, taking into account common challenges during interpretation, validation, and generalizability, and addressing safety and ethical concerns before final implementation.As investigators and clinicians continue to collaborate, AI in cardiology and in medicine in general will have a bright future.

Recent advancements in artificial intelligence (AI) applications in medicine have been significant over the past 30 years. To monitor current research developments, it is crucial to examine the latest trends in AI adoption across various medical fields. This bibliometric analysis focuses on AI applications in cardiology. Unlike existing literature reviews, this study specifically examines journal articles published in the last decade, sourced from both Scopus and Web of Science databases, to illustrate the recent trends in AI within cardiology. The bibliometric analysis involves a statistical and quantitative evaluation of the literature on AI application in cardiovascular medicine over a defined period. A comprehensive global literature review is conducted to identify key research areas, authors, and their interrelationships through published works. The leading institutions and most influential authors in research on the role of AI in cardiology were located in the United States, the United Kingdom, and China. This study also provides researchers with an overview of the evolution of research in AI and cardiology. The main contribution of this study is to highlight the prominent authors, countries, journals, institutions, keywords, and trends in the development of AI in cardiology.

Looking at the extremely large amount of literature, as summarized in two recent reviews on applications of Artificial Intelligence in Cardiology, both in the adult and pediatric age groups, published in the Journal of Clinical Medicine [...].

Artificial intelligence (AI) has an enormous potential for improving the quality of medical care, diagnostic methods, and treatments. AI allows taking scientific research to a fundamentally new level. The article addresses the most important areas of using AI in cardiology. AI can be used to accelerate making clinical decisions, remote patient monitoring, tomographic image analysis, patient phenotyping, including metabolomic analysis, to assess the risk of complications and many other areas.

Artificial Intelligence in Cardiology: Applications and Obstacles

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 (AI) provides machines with the ability to learn and respond the way humans do and is also referred to as machine learning. The step to building an AI system is to provide the data to learn from so that it can map relations between inputs and outputs and set up parameters such as “weights”/decision boundaries to predict responses for inputs in the future. Then, the model is tested on a second data set. This article outlines the promise this analytic approach has in medicine and cardiology.

Cardiovascular diseases remain the main cause of death worldwide which makes it essential to better understand, diagnose, and treat atherosclerosis. Artificial intelligence (AI) and novel technological solutions offer us new possibilities and enable the practice of individually tailored medicine. The study was performed using the PRISMA protocol. As of January 10, 2023, the analysis has been based on a review of 457 identified articles in PubMed and MEDLINE databases. The search covered reviews, original articles, meta-analyses, comments, and editorials published in the years 2009-2023. In total, 123 articles met inclusion criteria. The results were divided into the subsections presented in the review (genome-wide association studies, radiomics, and other studies). This paper presents actual knowledge concerning atherosclerosis, in silico, and big data analyses in cardiology that affect the way medicine is practiced in order to create an individual approach and adjust the therapy of atherosclerosis.

Significant progress in the field of Artificial Intelligence (AI) has been highlighted over the past decade. Its continuously evolving applications have found various uses in the medical field, focusing on prevention, screening, and treatment for a wide range of conditions, as well as anticipating their progression. In the field of cardiology, various AI models have proven their effectiveness in interpreting data from technologies such as electrocardiography and imaging, demonstrating their utility in interpreting echocardiography, nuclear magnetic resonance, as well as computer tomography. The integration of artificial intelligence into electrocardiogram (ECG) analysis not only improves the accuracy of diagnosis but also facilitates the recommendation of personalized and optimal treatment for each patient. Cardiovascular imaging has become an extremely advanced research field within AI, with echocardiography being an excellent method for non-invasive evaluation, both quantitatively and qualitatively, of cardiac function. The implementation of artificial intelligence in analyzing images obtained through nuclear magnetic resonance and computer tomography has also been successful in identifying specific conditions, such as myocardial ischemia or obstructive coronary artery disease.

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.

Artificial intelligence in cardiology and beyond

Much research in robotic artificial intelligence (AI) and Artificial Life has focused on autonomous agents as an embodied and situated approach to AI. Such systems are commonly viewed as overcoming many of the philosophical problems associated with traditional computationalist AI and cognitive science, such as the grounding problem (Harnad) or the lack of intentionality (Searle), because they have the physical and sensorimotor grounding that traditional AI was argued to lack. Robot lawn mowers and self-driving cars, for example, more or less reliably avoid obstacles, approach charging stations, and so on-and therefore might be considered to have some form of artificial intentionality or intentional directedness. It should be noted, though, that the fact that robots share physical environments with people does not necessarily mean that they are situated in the same perceptual and social world as humans. For people encountering socially interactive systems, such as social robots or automated vehicles, this poses the nontrivial challenge to interpret them as intentional agents to understand and anticipate their behavior but also to keep in mind that the intentionality of artificial bodies is fundamentally different from their natural counterparts. This requires, on one hand, a "suspension of disbelief " but, on the other hand, also a capacity for the "suspension of belief." This dual nature of (attributed) artificial intentionality has been addressed only rather superficially in embodied AI and social robotics research. It is therefore argued that Bourgine and Varela's notion of Artificial Life as the practice of autonomous systems needs to be complemented with a practice of socially interactive autonomous systems, guided by a better understanding of the differences between artificial and biological bodies and their implications in the context of social interactions between people and technology.

• The objectives of this study match the CIE content as it provides academcians and practioners with innovative and helpful information on advanced computing technology in the industry. • Foundation models are large-scale machine learning models that have been trained on large amounts of data to perform a variety of tasks. • FMs are used to support management and operations in supply chain and logistics management and operations. • The paper is based on research conducted by the authors with several Italian and European organizations under a grant from the European Union's Next GenerationUE initiative. • The paper concludes with a real-world industrial case study to further substantiate these claims. The organizational environment is subject to constant change, driven by rapid technological progress and the advent of Industry 5.0. This dynamic scenario compels authors and practitioners in the fields of logistics and supply chain management and operations (LSCM) to explore the transformative impact of foundation models (FMs) and artificial intelligence (AI) on these areas. The integration of AI FMs, coupled with digital twins (DT) and cyber-physical systems (CPS), will not only optimize current processes but also fundamentally reshape the future of digital engineering (DE) in LSCM. Understanding the opportunities and challenges with FMs is critical to the strategic positioning in an ever-evolving marketplace where Industry 5.0 technologies drive innovation and efficiency. Organizations can gain a competitive advantage by analyzing the opportunities and risks of integrating FMs into LSCM operations and leveraging their ability to improve decision making and enhance predictive analytics and real-time monitoring through DT and CPS. This paper serves as a comprehensive and informative guide for evaluating the transformative potential of FMs in redesigning LSCM operations in the context of Industry 5.0, highlighting critical applications, exploring potential benefits and challenges, and pointing to upcoming innovations such as AI-driven DT and smart cyber-physical systems that are revolutionizing supply chain (SC) processes. The detailed analysis based on the Supply Chain Operations Reference (SCOR) components shows that FM-based solutions, supported by AI and Industry 5.0 technologies, can significantly improve LSCM business processes. This paper presents an integrated functional framework and technical architecture for implementing FM in LSCM, emphasizing the role of DT and CPS in enabling smart, adaptive, and resilient SC. The paper concludes with an industrial case study that further substantiates these claims and shows the practical application of FM in a shipbuilding organization. The case study describes successful approaches to implementing and effectively utilizing AI and DT. It provides a practical, functional, and technical framework for applying the transformative impact of FMs on LSCM within the Industry 5.0 paradigm. This integration highlights the synergy between FM, AI, DT, and CPS and paves the way. It paves the way for a more innovative, sustainable, and competitive management of supply networks and their operations.

This study examines the potential of artificial intelligence (AI) to improve doctor-patient communication in resource-constrained public oncology settings in Kenya, addressing critical communication barriers that impact treatment outcomes and patient satisfaction. A qualitative exploratory study was conducted in three public oncology facilities in Nairobi County, employing semi-structured in-depth interviews with 10 clinical officers and physicians. Data were analysed using thematic analysis with NVivo 12 software to identify patterns and themes related to communication challenges and the potential for AI integration. Three major themes emerged from the analysis: significant communication barriers, including the complexity of technical language, time constraints, and cultural mismatches; high clinician interest in AI communication tools despite limited current awareness; and critical requirements for the ethical implementation of AI systems, including the need for explainable AI, cultural adaptation, and preservation of human empathy. A majority of participants (8 out of 10) identified technical language barriers as the most significant challenge, while all participants (10 out of 10) expressed high interest in AI applications such as medical jargon translation, patient distress detection, and real-time conversational support. This study provides the first empirical evidence for a clinician-endorsed, empathy-centric framework for AI integration in resource-constrained settings, positioning AI not as a replacement but as a tool for augmenting human connection. The core conceptual contribution demonstrates AI as an "empathy enhancer" that addresses systemic communication barriers while preserving the relational aspects of healthcare. The findings provide a culturally appropriate, phased implementation framework for AI communication tools in African healthcare contexts, emphasising comprehensive training and ethical oversight. AI-augmented communication has the potential to improve health outcomes, reduce healthcare disparities, and enhance patient satisfaction in resource-constrained settings, directly supporting Kenya's UHC goals by addressing a key social determinant of health: communication. This study offers crucial insights for the development of healthcare technology in similar resource-constrained environments.