Vetnews | Oktober 2024 12 « BACK TO CONTENTS Abstract Background and Aim: In recent years, artificial intelligence (AI) has become increasingly necessary in the life sciences, particularly medicine and healthcare. This study aimed to systematically review the literature and critically analyze multiple databases on the use of AI in veterinary medicine to assess its challenges. We aim to foster an understanding of the effects that can be approached and applied for professional awareness. Materials and Methods: This study used multiple electronic databases with information on applied AI in veterinary medicine based on the current guidelines outlined in PRISMA and Cochrane for systematic review. The electronic databases PubMed, Embase, Google Scholar, Cochrane Library, and Elsevier were thoroughly screened through March 22, 2023. The study design was carefully chosen to emphasize evidence quality and population heterogeneity. Results: A total of 385 of the 883 citations initially obtained were thoroughly reviewed. There were four main areas that AI addressed; the first was diagnostic issues, the second was education, animal production, and epidemiology, the third was animal health and welfare, pathology, and microbiology, and the last was all other categories. The quality assessment of the included studies found that they varied in their relative quality and risk of bias. However, AI aftereffect-linked algorithms have raised criticism of their generated conclusions. Conclusion: Quality assessment noted areas of AI outperformance, but there was criticism of its performance as well. It is recommended that the extent of AI in veterinary medicine should be increased, but it should not take over the profession. The concept of ambient clinical intelligence is adaptive, sensitive, and responsive to the digital environment and may be attractive to veterinary professionals as a means of lowering the fear of automating veterinary medicine. Future studies should focus on an AI model with flexible data input, which can be expanded by clinicians/users to maximize their interaction with good algorithms and reduce any errors generated by the process. Introduction Medicine has always been and will likely remain an average profession, wherein offered treat- ments correspond to the most effective plan for the average patient. Individual variation might negate this assumption, and as a result, false positive and false negative results might arise. The more the treatment process can be digitalized, the more pre- cise outcomes can become. In recent years, artificial intelligence (AI) has become increasingly necessary in the life sciences, particularly in medicine and healthcare. Chang [1] reviewed the main areas of AI focus, which included advantages for imaging interpretation using deep-machine learning (ML), which can help with decision-making, digitalization, which can aid in administrative support and natural language process- ing for communication, and education and training, which can be used for data mining, risk assessment, and prediction. Artificial intelligence has been widely adopted and applied in veterinary medicine to animals’ healthcare by maximizing predictive indicators and achieving greater accuracy in diagnosis. Machine learning interacts with imaging, pathology slides, and patients’ electronic medical records to aid in reaching the correct diagnosis, prescribing appropriate therapy, and augmenting professionals’ capabilities [2]. Several areas have attempted to improve diagnosis and disease control through the application of AI. Laboratory haematology analyzers and imaging machines include AI expert systems, and mathematical algorithms use raw input data to provide clinical interpretation [3]. At present, there are growing concerns regarding the comparison of clinicians to AI algorithms, and to what extent do AI outcomes support an accurate clinical decision. For instance, based on slide scanning, digital pathology is more accurate than humans evaluating high-resolution slides. However, veterinarians link these AI findings to the patient’s clinical background before making further decisions. Artificial intelligence tools developed for this field have a diagnostic accuracy of up to 95% and are almost 100 times faster in providing results [4, 5]. In this study, we intend to qualitatively and quantitatively describe the current state of applied AI in veterinary medicine, elucidate future trends, and critically interpret outcomes of those fields that have applied Hi-Tech methods. To the best of our knowledge, there has been no published systematic literature review on the use of AI in veterinary medicine. Therefore, this study aimed to review and critically analyze the literature in different databases and offer a qualitative assessment of these findings with a descriptive analysis of applied AI in veterinary medicine. Artificial intelligence feasibility in veterinary medicine: A systematic review Fayssal Bouchemla1 , Sergey Vladimirovich Akchurin2, Irina Vladimirovna Akchurina2 , Georgiy Petrovitch Dyulger2, Evgenia Sergeevna Latynina2, and Anastasia Vladimirovna Grecheneva3 Corresponding author: Fayssal Bouchemla, e-mail: faysselj18@yahoo.com Co-authors: ASV: sakchurin@rgau-msha.ru, AIV: sakchurin@rgau-msha.ru, GPD: dulger@rgau-msha.ru, LES: evgenialatynina@rgau-msha.ru, GAV: a.grecheneva@rgau-msha.ru 1. Department of Animal Disease, Veterinarian and Sanitarian Expertise, Faculty of Veterinary Medicine, Vavilov Saratov State University of Genetic, Biotechnology and Engineering Saratov, Russia; 2. Department of Veterinary Medicine, Russian State Agrarian University- Moscow Agricultural Academy named after K.A. Timiryazev, 49, str. Timiryazevskaya, Moscow, 127550, Russia; 3. Department of Applied Informatics, Russian State Agrarian UniversityMoscow Agricultural Academy named after K.A. Timiryazev, 49, str. Timiryazevskaya, Moscow, 127550, Russia.
RkJQdWJsaXNoZXIy OTc5MDU=