Prospects for the generative artificial intelligence application in surgery, traumatology and orthopedics

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Abstract

The review considers the use of generative artificial intelligence technologies in surgery, traumatology and orthopedics. Definitions of key generative artificial intelligence technologies are given, as well as the difference between discriminative and generative models of artificial intelligence. An analysis of publication activity on the use of generative artificial intelligence in surgery, traumatology and orthopedics in world macroregions is conducted. The potential role of various generative artificial intelligence models at the preoperative, intraoperative and postoperative stages of healthcare is analyzed. Data on the results of clinical application of generative artificial intelligence and the most common problems associated with the practical use of various generative artificial intelligence applications are provided including issues of quality and safety of surgical care. The review proposes potential solutions and research directions to address these problems.

About the authors

Anton G. Nazarenko

Priorov National Medical Research Center for Traumatology and Orthopedics

Email: NazarenkoAG@cito.priorov.ru
ORCID iD: 0000-0003-1314-2887
SPIN-code: 1402-5186

MD, Dr. Sci. (Medicine), рrofessor RAS

Russian Federation, 9 Novospassky per., 115172 Moscow

Elena B. Kleimenova

Priorov National Medical Research Center for Traumatology and Orthopedics

Email: KleymenovaEB@cito-priorov.ru
ORCID iD: 0000-0002-8745-6195
SPIN-code: 2037-7164

MD, Dr. Sci. (Medicine), рrofessor

Russian Federation, 9 Novospassky per., 115172 Moscow

Nodari M. Kakabadze

Priorov National Medical Research Center for Traumatology and Orthopedics

Email: KakabadzeNM@cito-priorov.ru
ORCID iD: 0000-0002-2380-2394
SPIN-code: 6321-6733
Russian Federation, 9 Novospassky per., 115172 Moscow

Alexey I. Molodchenkov

Federal Research Center «Computer Science and Control» of the Russian Academy of Sciences; Peoples’ Friendship University of Russia

Email: aim@isa.ru
ORCID iD: 0000-0003-0039-943X
SPIN-code: 3378-7234

Cand. Sci. (Engineering)

Russian Federation, Moscow; Moscow

Liubov P. Yashina

Priorov National Medical Research Center for Traumatology and Orthopedics

Author for correspondence.
Email: YashinaLP@cito-priorov.ru
ORCID iD: 0000-0003-1357-0056
SPIN-code: 1910-0484

Cand. Sci. (Biology)

Russian Federation, 9 Novospassky per., 115172 Moscow

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2. Fig. 1. The most common models and algorithms of generative artificial intelligence in medicine (adapted from [17]). Note. ИИ — artificial intelligence, МО — machine learning, Д-ИИ — discriminative artificial intelligence, Г-ИИ — generative artificial intelligence, БЯМ — large language models, СНС — convolutional neural network, РНС — recurrent neural network, СДКП — long short-term memory network, ГСС — generative adversarial networks, САК — adversarial autoencoder, РАК — sparse autoencoder, ВАК — variational autoencoder.

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3. Fig. 2. Publication activity of authors from macro-regions of the world on the topic of generative artificial intelligence in surgery, traumatology and orthopedics.

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4. Fig. 3. Application of generative artificial intelligence technologies in surgery (adapted from [17]). Note. ЭМК — electronic health record.

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