Background/Objectives: Diabetic retinopathy (DR) is a progressive microvascular complication of diabetes mellitus and a leading cause of vision impairment worldwide. Early detection and timely management are critical in preventing vision loss, yet current screening programs face challenges, including limited specialist availability and variability in diagnoses, particularly in underserved areas. This literature review explores the evolving role of artificial intelligence (AI) in enhancing the diagnosis, screening, and management of diabetic retinopathy. It examines AI’s potential to improve diagnostic accuracy, accessibility, and patient outcomes through advanced machine-learning and deep-learning algorithms. Methods: We conducted a non-systematic review of the published literature to explore advancements in the diagnostics of diabetic retinopathy. Relevant articles were identified by searching the PubMed and Google Scholar databases. Studies focusing on the application of artificial intelligence in screening, diagnosis, and improving healthcare accessibility for diabetic retinopathy were included. Key information was extracted and synthesized to provide an overview of recent progress and clinical implications. Conclusions: Artificial intelligence holds transformative potential in diabetic retinopathy care by enabling earlier detection, improving screening coverage, and supporting individualized disease management. Continued research and ethical deployment will be essential to maximize AI’s benefits and address challenges in real-world applications, ultimately improving global vision health outcomes.

An abdominal aortic aneurysm (AAA) is a pathological dilation that is 3 cm or greater resulting in a bulging or balloon appearance. To meet a personalized therapeutic approach for patients, artificial intelligence (AI) can exhibit an array of applications ranging from decoding patterns from large data sets to predicting new data. The review aims to discuss how AI can assist and improve the standard of care and management plans for these patients. A comprehensive non-systematic literature review was carried out for published material on the use of AI relating to AAAs. The PubMed and Google Scholar databases were used to scout for articles relating to the title of this review. The review included 54 literature papers in this study. AI is involved on a genomic level, which assists in screening, diagnosing, and identifying individual risk factors of a patient. Personalized management plans can be created with AI predictions using patient data to reduce the risk of in-hospital mortality following a repair or due to complications. AI represents a promising group of programs aimed at improving patient management and assisting surgeons in making beneficial decisions to improve the patient’s prognosis.

Background/Objectives: Artificial intelligence (AI) is increasingly being integrated into vascular surgery, particularly in diagnostic imaging, perioperative planning, intraoperative guidance, and postoperative surveillance. This literature review evaluates the current applications of artificial intelligence in vascular surgery and endovascular practice, with a particular focus on imaging technologies and their role in improving diagnostic precision, workflow efficiency, and patient outcomes. In addition, the review examines emerging AI applications in operative workflow optimization, endovascular navigation, postoperative surveillance, training platforms, and AI-assisted clinical decision support. Methods: A literature review was conducted using PubMed and Scopus with the search terms: (artificial intelligence OR AI OR neural network) AND (vascular surgery) AND (diagnosis OR treatment). Reference lists of included studies were manually screened, and additional recent studies were identified from relevant journals. Articles published in English up to April 2026 were included. Studies were assessed for their applications in vascular diagnostics, plaque characterization, endovascular workflow optimization, and postoperative surveillance. Results: AI demonstrated strong diagnostic performance across multiple imaging modalities. Deep learning systems achieved a sensitivity of 91.3% and specificity of 95.2% in peripheral arterial stenosis classification, while plaque characterization models showed accuracies up to 96% and substantial agreement with expert imaging interpretation. AI-assisted operative systems improved procedural efficiency through reductions in operative duration, radiation exposure, and contrast utilization. However, many studies were retrospective, single-center, and based on relatively small cohorts with heterogeneous endpoints. Conclusions: AI has significant potential to improve vascular surgical practice through enhanced image interpretation, procedural guidance, and individualized treatment planning. Despite promising outcomes, current evidence remains limited by methodological heterogeneity and insufficient external validation. Prospective multicenter studies and standardized evaluation frameworks are required before widespread clinical implementation can be achieved.

Several studies have found a clear association between higher PTH levels and increased mortality risk in hemodialysis patients. The studies indicate that higher levels of PTH are linked to a higher risk of cardiovascular events, such as heart attacks and strokes, as well as an increased risk of bone fractures.