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This article is cited in 1 scientific paper (total in 1 paper)
Scientific Part
Computer Sciences
Possibilities of using computer vision for data analytics in medicine
O. Yu. Iliashenko, E. L. Lukyanchenko Peter the Great Saint Petersburg Polytechnic University, 29 Polytechnicheskaya St., St. Petersburg 195251, Russia
Abstract:
This article discusses the possibilities of using artificial intelligence technologies, namely computer vision, in the field of medicine. The relevance of the topic is due to the growing burden on medical personnel and medical institutions due to an increase in the number of elderly people, an increase in the number of patients with chronic diseases, as well as unforeseen circumstances, such as the SARS-CoV-2 pandemic in 2019–2021. In addition, many medical institutions are interested in providing high-quality services, increasing loyalty, and increasing the number of regular patients, and therefore feel the need to introduce the latest technologies and follow strategic development trends. The article describes how the physician can use the solutions offered by artificial intelligence in the course of his work to obtain a more accurate diagnosis and save time spent on the patient's history review. The authors propose an IT and technological architecture of a medical organization that uses computer vision in its work, created on the basis of the IT and the technological architecture reference model of a medical organization. The architecture implies the use of cloud infrastructure and specialized software and provides for both the introduction of new types of equipment, for example, 3D cameras, imaging sensors, and the use of traditional equipment: an ultrasound machine, X-ray equipment, an MRI machine.
Key words:
artificial intelligence, computer vision, enterprise architecture, IT architecture, medicine.
Received: 25.11.2021 Accepted: 27.12.2021
Citation:
O. Yu. Iliashenko, E. L. Lukyanchenko, “Possibilities of using computer vision for data analytics in medicine”, Izv. Saratov Univ. Math. Mech. Inform., 22:2 (2022), 224–232
Linking options:
https://www.mathnet.ru/eng/isu936 https://www.mathnet.ru/eng/isu/v22/i2/p224
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Abstract page: | 453 | Full-text PDF : | 48 | References: | 18 |
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