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Use of artificial intelligence technologies in laboratory medicine, their effectiveness and application scenarios: a systematic review
Vasilev Y.A., Nanova O.G., Vladzymyrskyy A.V., Goldberg A.S., Blokhin I.A., Reshetnikov R.V.
Assessment of chewing efficiency with artificial intelligence
Levashov N.E., Gus’kov A.V., Oleynikov A.A., Domashkevich N.S.
The concept of responsible artificial intelligence as the future of artificial intelligence in medicine
Germanov N.S.
Modern capabilities of artificial intelligence technologies in cardiovascular imaging
Islamgulov A.K., Bogdanova A.S., Sufiiarov D.I., Chernyavskaya A.V., Bairakaeva E.R., Maksimova A.A., Nemychnikov N.V., Bikieva D.R., Shakhmaeva A.I., Burdina L.A., Bolekhan A.V., Akimov E.I., Shurakova Z.Z.
Machine-learning technology for predicting intraocular lens power: Diagnostic data generalization
Arzamastsev A.А., Fabrikantov O.L., Zenkova N.А., Belikov S.V.
Machine learning techniques for breast cancer diagnosis
Dyomin K.S., Germashev I.V.
Machine-learning and artificial neural network technologies in the classification of postkeratotomic corneal deformity
Tsyrenzhapova E.K., Rozanova O.I., Iureva T.N., Ivanov A.A., Rozanov I.S.
Classification of optical coherence tomography images using deep machine-learning methods
Arzamastsev A.A., Fabrikantov O.L., Kulagina E.V., Zenkova N.A.
Evolution of research and development in the field of artificial intelligence technologies for healthcare in the Russian Federation: results of 2021
Gusev A.V., Vladzymyrskyy A.V., Sharova D.E., Arzamasov K.M., Khramov A.E.
Use of artificial intelligence in the diagnosis of arterial calcification
Trusov Y.А., Chupakhina V.S., Nurkaeva A.S., Yakovenko N.A., Ablenina I.V., Latypova R.F., Pitke A.P., Yazovskih A.A., Ivanov A.S., Bogatyreva D.S., Popova U.A., Yuzlekbaev A.F.
MosMedData: data set of 1110 chest CT scans performed during the COVID-19 epidemic
Morozov S.P., Andreychenko A.E., Blokhin I.A., Gelezhe P.B., Gonchar A.P., Nikolaev A.E., Pavlov N.A., Chernina V.Y., Gombolevskiy V.A.
Using neural networks for non-invasive determination of glycated hemoglobin levels, illustrated by the application of an innovative portable glucometer in clinical practice
Poliker E.E., Koshechkin K.A., Timokhin A.M., Klyukina E.V., Belyakova E.D., Brovko A.M., Lalayan A.S., Ermolaeva A.S.
Dosiomics in the analysis of medical images and prospects for its use in clinical practice
Solodkiy V.A., Nudnov N.V., Ivannikov M.E., Shakhvalieva E.S., Sotnikov V.M., Smyslov A.Y.
Development of a portable spectrophotometer using artificial neural networks for non-invasive determination of glycated hemoglobin in blood by Raman spectroscopy
Poliker E.E., Zemskikh B.L., Koshechkin K.A.
Prospects of machine learning applications in affective disorders
Mosolova E.S., Alfimov A.E., Kostyukova E.G., Mosolov S.N.
1 - 15 of 15 Items

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