Abstract
Consultation prioritization is fundamental in optimal healthcare management and its performance can be helped by artificial intelligence (AI)-dedicated software and by digital medicine in general. The need for remote consultation has been demonstrated not only in the pandemic-induced lock-down but also in rurality conditions for which access to health centers is constantly limited. The term “AI” indicates the use of a computer to simulate human intellectual behavior with minimal human intervention. AI is based on a “machine learning” process or on an artificial neural network. AI provides accurate diagnostic algorithms and personalized treatments in many fields, including oncology, ophthalmology, traumatology, and dermatology. AI can help vascular specialists in diagnostics of peripheral artery disease, cerebrovascular disease, and deep vein thrombosis by analyzing contrast-enhanced magnetic resonance imaging or ultrasound data and in diagnostics of pulmonary embolism on multi-slice computed angiograms. Automatic methods based on AI may be applied to detect the presence and determine the clinical class of chronic venous disease. Nevertheless, data on using AI in this field are still scarce. In this narrative review, the authors discuss available data on AI implementation in arterial and venous disease diagnostics and care.
Highlights
The need for optimizing healthcare management by guaranteeing both top quality consultation and the rationalization of the available infrastructure resources is of paramount importance, in a post COVID-19 pandemic world [1]
Might remote consultation be helpful in such situations as a pandemic-induced lock-down, but it may be of use in rural areas with a limited access to well-equipped healthcare centers [2]
Diagnostic tools based on artificial intelligence (AI)-dedicated software and digital medicine in general are considered an effective solution for remote consultations [1,3]
Summary
The need for optimizing healthcare management by guaranteeing both top quality consultation and the rationalization of the available infrastructure resources is of paramount importance, in a post COVID-19 pandemic world [1]. AI is based on a machine learning process or on an artificial neural network (ANN). AI can be used to support cost-effective clinical decision-making, developing healthcare recommender systems, emotion recognition using physiological signals, and patient monitoring [3,11,12]. This can significantly reduce the burden on healthcare workers, maximizing their time and expertise for optimal patient care [13]. AI has already been used in diagnostics for “object detection” (lesion localization), “object segmentation” (determination of the contours and boundaries of the lesion), and “classification of objects” (malignant or benign) [14,15] This makes AI useful in radiology, where large datasets are processed [16]. The present narrative review reports the current state of the art of AI in the management of arterial disease, venous thromboembolism (VTE), and chronic venous disease (CVD)
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