Background: Digital transformation in cardiology is reshaping diagnosis, monitoring, and therapy through connected devices, digital biomarkers, telemedicine, and artificial intelligence. This paper synthesizes recent advances and emphasizes the shift from episodic assessment to continuous surveillance and personalized predictive care. Methods: The article included 81 studies published between 2015 and 2026, identified in PubMed, Google Scholar, and ScienceDirect, encompassing clinical trials, meta- analyses, observational studies, and reviews focused upon continuous cardiovascular monitoring, remote care, and AI-supported analysis. According to the selection criteria, studies with weak methodology or limited clinical relevance were removed. Results: Continuous monitoring enables early detection of physiological changes, individualized prevention, reduces hospitalizations, and supports cost efficiency and the expansion of remote services. Artificial intelligence and multimodal analytics facilitate dynamic risk stratification, clinical decision support, and the development of cardiovascular digital twin models for precision medicine. Implementation remains constrained by requirements for robust validation, potential algorithmic bias, data protection and cybersecurity concerns, and regulatory or acceptance barriers. Conclusions: Digital cardiology enables anticipatory, patient- centered healthcare based on continuous monitoring and AI as core clinical tools, promoting integrated patient–hospital–home ecosystems, real-time therapeutic guidance, and sustained cardiovascular prevention. The priorities defined by these directions are subjects for further research and healthcare development worldwide in the next decades. improves
Artificial Intelligence (AI) has become an important tool for computer-aided diagnosis of medical imaging. This review aims to provide an overview for clinicians, explaining the relevant aspects of artificial intelligence and machine learning (ML) and presenting up-to-date applications of AI and ML techniques to medical imaging methods such as angiography, magnetic resonance, and echocardiography. For each imaging method, we present the acquisition process, the types of diagnostic test interpretation, and the challenges related to them, as well as how AI/ML techniques, have improved the process of decision making. A summary of selected works applying AI/ML techniques to medical imaging is organized into a table, which highlights the scope of the study, the dataset used, the details of each approach as well as the measured results, including objectives and criteria. The overall benefits of AI in medical imaging are extracted based on the diverse applications and high evaluation scores. In the end, cardiologists should have an advanced understanding of using AI to integrate clinical data and making the final decision in diagnosis.
