Cardiac amyloidosis was long considered rare and untreatable, but both assumptions have given way, and the disease is now recognized as an underdiagnosed and increasingly treatable cause of heart failure. The available therapies work by slowing amyloid deposition, so their benefit depends on early diagnosis, yet the condition is still identified late and usually only after referral for specialized imaging. Artificial intelligence has started to shorten this path. Models built on the electrocardiogram and the echocardiogram detect amyloid infiltration with high accuracy and have reached external validation, while artificial-intelligence-based analysis of heart sound, the cheapest and most widely available cardiac signal, has not yet been directly evaluated in this disease. The physiological rationale for studying it is strong, because a ventricle stiffened by amyloid is expected to alter the low-frequency diastolic sounds that reflect ventricular compliance, although whether these changes are specific to amyloidosis remains unknown. This review brings together the acoustic manifestations of cardiac amyloidosis, the signal-processing and machine learning methods relevant to heart sounds, the clinical evidence across imaging and acoustic modalities, and the practical and equity questions that deployment raises. Heart sound analysis remains the most scalable and least explored opportunity in the field. We outline the mechanistic rationale, the available methods, the lessons from the more mature electrocardiographic and echocardiographic literature, and a set of minimum design requirements for a first disease-specific study.

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.

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.

Background/Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have emerged as a promising therapeutic option for women with polycystic ovary syndrome (PCOS). Clinical trials and data demonstrate significant improvements in body weight, insulin resistance, hyperandrogenism, and, in some studies, menstrual regularity and reproductive outcomes. However, the current evidence base remains disproportionately focused on short-term metabolic and anthropometric endpoints, while outcomes most relevant to patients—including treatment satisfaction, gastrointestinal tolerability, psychological well-being, body image, fertility planning, financial burden, and long-term adherence—remain inadequately characterized. Methods: We searched PubMed and Google Scholar for English-language literature published between 2021 and 2026, using structured keyword searches. This narrative review evaluates the literature on GLP-1RA use in PCOS beyond weight-focused outcomes, examining mechanisms of action, comparative tolerability across agents, and patient-centered domains including quality of life, eating behavior, and reproductive planning. Results: The published literature reveals a substantial gap between trial-reported completion rates and treatment success, and actual persistence and patient satisfaction. This discrepancy is particularly consequential in PCOS, where treatment decisions intersect with high baseline rates of depression, anxiety, and body image disturbance, and where ovulatory benefits must be weighed against mandatory discontinuation before conception. Conclusions: We identify a systematic absence of validated patient-reported outcome measures, standardized core outcome sets, and long-term follow-up data after treatment withdrawal. This review proposes a research agenda prioritizing patient-centered endpoints as co-primary outcomes, structured assessment of acceptability and persistence, qualitative and mixed-methods approaches, and a dedicated study of fertility planning and treatment interruption.

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.