AI Article Synopsis
- Consultation prioritization is essential for effective healthcare management, and AI-powered software can enhance this process by enabling remote consultations, especially in rural areas or during pandemics.
- AI mimics human intelligence using machine learning and neural networks, which helps create precise diagnostic algorithms and tailored treatments in various medical fields like oncology and dermatology.
- The authors review existing research on the role of AI in diagnosing and treating arterial and venous diseases, noting that while there are promising applications, data on its effectiveness in these areas remain limited.
Article 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8705683 | PMC |
| http://dx.doi.org/10.3390/jpm11121280 | DOI Listing |
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