Background: Varicose veins (VVs) of the lower limbs, characterized by palpable, dilated, and tortuous veins, affect 2–73% of the global population. Artificial intelligence (AI) offers significant potential to enhance healthcare efficiency and decision-making, particularly in managing VVs through improved risk factor identification, diagnosis, and treatment planning. Objective: This abstract explores the role of AI in VV management, focusing on its applications in risk detection, image analysis, treatment planning, and surgical interventions, while addressing challenges to its widespread adoption. Methods: AI leverages advanced techniques such as computer vision and deep learning to analyze patient data, including medical history, symptoms, physical examinations, and imaging (e.g., ultrasounds, venography). It identifies patterns in large datasets to support personalized treatment plans, early risk detection, and disease severity assessment. Results: AI demonstrates promise in automating VV detection and classification, assessing disease severity, and aiding treatment planning. It enhances surgical interventions through preoperative planning, intraoperative navigation, and recurrence risk prediction. However, its adoption is limited by a lack of large-scale studies, concerns over accuracy, and the need for regulatory and ethical oversight. Conclusion: AI has the potential to revolutionize VV management by improving diagnosis, treatment precision, and patient outcomes. Further research, validation, and integration are critical to overcoming current limitations and fully realizing AI’s capabilities in clinical practice.

Hypertension has been traditionally known to be highlighted by mean blood pressure; however, emerging evidence exhibits that blood pressure variability (BPV), including short-term, day-to-day, and visit-to-visit fluctuations can have an implication across multiple body systems. Elevated BPV reflects repetitive hemodynamic stress, affecting the physiologic hemostasis contributing to vascular injury and end organ damage. This narrative review is a compilation of recent evidence on the prognostic value of BPV, explained by pathophysiology, various devices with its measurement approaches, and, essentially, the clinical implication of BPV and the use of such devices utilizing artificial intelligence. A comprehensive literature search across PubMed, Cochrane Library, Scopus, and Web of Science were conducted, focusing on observational studies, cohorts, randomized trials, and meta-analyses. Higher BPV has been associated with an increased risk of cardiovascular mortality, stroke, coronary events, and heart failure, the progression of chronic kidney disease, cognitive decline, and preeclampsia, among other end organ damage, despite mean blood pressure. The various pathophysiologic mechanisms include autonomic dysregulation, arterial stiffness, endothelial dysfunction, circadian rhythm alteration, and systemic inflammation, which result in vascular remodeling and multisystem damage. Antihypertensive medications such as calcium channel blockers and renin–angiotensin–aldosterone system inhibitors seem to reduce BPV; randomized trials have not specifically investigated their BPV-reducing effects. The aim of this review is to highlight that BPV is a dynamic marker of multisystem risk, and question how various AI-based devices can aid continuous BPV monitoring and patient specific risk stratification.

Giant cell arteritis (GCA) is the most common primary systemic vasculitis of older adults and has traditionally been framed as an ophthalmologic emergency. Its most serious consequences, however, occur in the cardiovascular system, often years after inflammatory markers have normalized. In contemporary matched population cohorts, patients with GCA carry an approximately two-fold excess risk of aortic aneurysm; the historical Olmsted County cohort reported a 17-fold excess of thoracic aortic aneurysm with wide confidence limits, and the characteristic latency to aneurysm is five to ten years. Patients also face elevated risks of aortic dissection, myocardial infarction, stroke, venous thromboembolism, and heart failure that cluster within the first year of diagnosis, when vascular inflammation is most active. This narrative review, based on a structured search of PubMed/MEDLINE and Google Scholar through September 2026 with independent verification of every cited estimate, synthesizes current evidence on the full spectrum of cardiac and aortic complications of GCA, including aortitis, aneurysm and dissection, coronary arteritis, valvular disease, pericarditis, myocarditis, and conduction disease. We trace their common origin to the immunopathology of the arterial wall, where dendritic cell activation, Th1/Th17 polarization, and maladaptive vascular remodeling produce occlusion in medium-sized arteries and dilation in the aorta. We examine current diagnostic modalities, from the ultrasound-based fast-track clinic, which has reduced permanent visual loss by half or more in observational cohorts, to FDG-PET/CT, in which aortic uptake at diagnosis is associated with subsequent aortic dilation. We review treatment after the GiACTA and SELECT-GCA trials in the light of the 2025 European Alliance of Associations for Rheumatology (EULAR) recommendations, the unresolved role of antiplatelet and statin therapy, and the surgical and endovascular management of GCA-related aortopathy. We suggest that the organization of care, as well as the limitations of drug therapy, may contribute to the residual mortality of GCA, a hypothesis that has not been tested; distinguish recommendations that are supported by current guidelines from those that are our own proposals; and outline a cardio-rheumatology pathway in which baseline vascular phenotyping informs individualized aortic surveillance. GCA is a disease whose natural history often begins in the rheumatology clinic and ends in the cardiac operating room without prior surveillance.

Background: Electrical and dielectric properties of the myocardium depend on its composition, fibrosis, edema, ischemia, and heat damage, and hence may play an important role in defining the electrophysiologic substrate. Local impedance and other impedance-based methods give information about these parameters that is available to clinicians, but not direct or substitutable for intrinsic conductivity and permittivity of the tissue. The use of artificial intelligence has been thoroughly studied for the ECGs, electrograms, images, and computational data analysis, but not for dielectric or impedance measurements. Objective: The study aims to review the scientific basis of dielectric properties of heart muscle critically in cardiology and electrophysiology, to differentiate between existing clinical and conceptual applications of myocardial dielectrics, and to estimate current and potential contributions of artificial intelligence to their interpretation. Data Sources and Study Selection: The current work represents a structured narrative review in PubMed, Scopus, and Google Scholar databases from inception until May 2026 based on various combinations of keywords that refer to myocardial impedance, dielectric properties, conductivity, permittivity, local impedance, dielectric imaging, cardiac electrophysiology, catheter ablation, machine learning, and artificial intelligence. This review included experimental, ex vivo, animal, computational, and human studies related to myocardial dielectric properties and their use in electrophysiology. Evidence was classified according to four categories: direct human clinical evidence; preclinical/translation evidence; indirect evidence from related techniques; and extrapolation concept. As a result of heterogeneity between studies, sensing technologies, and outcomes, the results have been analyzed narratively. Key Findings: Evidence regarding the usage of AI algorithms, which have been primarily trained on myocardial dielectric or catheter-based local impedance data, is extremely scarce and mostly consists of preclinical or ex vivo data. On the contrary, the majority of clinically used AI algorithms in the field of electrophysiology make use of ECG data, intracardiac electrogram data, image processing, or computational modeling without any inputs related to dielectrics or local impedance. Conclusions and Implications for Practice: Dielectric sensing is an attractive supplement to traditional methods of voltage mapping and imaging, although the clinical readiness of dielectric sensing greatly varies depending on the application. At the present time, the existing data provide a basis for utilizing the local impedance measurement in the ablation procedure and evaluation of the myocardium properties, while the combination of artificial intelligence and dielectric sensing is still experimental. Before being introduced into the clinical electrophysiology practice, signal acquisition standardization and prospective multicenter trials are needed.

Photoplethysmography (PPG) is a non-invasive optical technique commonly used to measure heart rate and oxygen saturation, but its waveform contains additional physiological information that can be analyzed using artificial intelligence (AI). This narrative review summarizes the emerging applications of AI-based PPG in cardiovascular, respiratory, sleep, hemodynamic, pregnancy-related, and portal-hypertension assessment, with the aim of evaluating its potential beyond conventional monitoring and identifying barriers to clinical translation. The literature search was conducted using PubMed, Google Scholar, IEEE Xplore, ScienceDirect, and SpringerLink. Additional relevant studies were identified through screening the reference lists of included articles. Studies published between 2002 and 2026 were identified to capture the development of PPG from conventional monitoring to newer AI-based applications. Human studies were prioritized, while relevant computational, simulated, synthetic, ex vivo, and technical studies were also included. Studies unrelated to PPG, duplicates, and studies with limited relevance were excluded. A total of 96 references were included, covering AI approaches such as convolutional and deep neural networks, ensemble methods, transfer learning, U-Net, generative adversarial networks, and Transformer-based models. Overall, the reviewed evidence suggests that AI-based PPG may support blood pressure estimation, atrial fibrillation detection, sleep and respiratory monitoring, vascular aging assessment, pulmonary hypertension screening, preeclampsia assessment, volume-status and compensatory-reserve assessment, and exploratory assessment related to portal hypertension. However, clinical translation remains limited by motion artifacts, sensor and measurement-site variability, skin-pigmentation-related bias, physiological and environmental influences, heterogeneous methods, limited external validation, and inconsistent clinical and regulatory standards.

Cardiorenal syndrome (CRS) encompasses a spectrum of disorders arising from bidirectional interactions between the heart and kidneys and is associated with substantial morbidity, mortality, and healthcare burden. Early recognition remains challenging because conventional biomarkers and imaging modalities may inadequately detect subclinical disease or distinguish functional changes from true organ injury. This narrative review examines the epidemiology, classification, pathophysiology, diagnostic approaches, and contemporary management of CRS, with particular emphasis on the emerging role of artificial intelligence (AI). Relevant evidence regarding established and emerging diagnostic and therapeutic approaches was reviewed, including biomarkers, imaging, guideline-directed medical therapy, ultrafiltration, cardiac devices, transplantation, and AI-based applications. Current evidence demonstrates the multifactorial nature of CRS and highlights the importance of early diagnosis and individualized management. Machine learning and deep learning approaches have shown potential to integrate electronic health records, biomarkers, imaging, physiological signals, and multi-omics data to support early detection, risk stratification, outcome prediction, and therapeutic decision-making. AI-assisted approaches may complement conventional diagnostic and management strategies and facilitate more precise and proactive CRS care. However, limitations related to data quality, model interpretability, external validation, and clinical implementation must be addressed before widespread clinical adoption.