Öz
The use of artificial intelligence in veterinary sciences has placed studies on this subject in a significant position in the literature. The increasing number of studies using artificial intelligence algorithms in the current literature shows that knowledge discovery in this field is increasing rapidly. This study aims to provide a general map of the literature on the utilization of artificial intelligence in veterinary medicine science and identify its application areas using bibliometric analysis. Web of Science database was used to reveal the current literature about artificial intelligence in veterinary medicine. The data were analyzed using the “Bibliometrix” package in the R statistical programming language and the VOSviewer program. Various research elements, including journals, article-citation counts, authors, institutes, and countries, were examined using bibliometric metrics. The number of studies on artificial intelligence in veterinary medicine from increased dramatically since 2019. According to the findings, the most influential countries identified were the USA, China, and Türkiye. Animals and Preventive Veterinary Medicine were determined as the most influential journals in the field. The findings indicated that artificial intelligence in veterinary medicine is a trending topic. The topics “deep learning”, “active learning”, and “computer-aided diagnosis” were estimated to be increasingly utilized soon. Rapid developments in artificial intelligence will likely attract more researchers to the field. This article, the first bibliometric study about the utilization of artificial intelligence in animal sciences, will offer researchers valuable information about the intellectual structure of the field.
Anahtar Kelimeler
Artificial intelligence Bibliometric analysis Classification problem Machine learning Veterinary sciences
Etik Beyan
Ethics committee permissions for this study were obtained from Selçuk University Faculty of Veterinary Medicine, Experimental Animal Production and Research Centre Ethics Committee and the study was carried out within the scope of the permission of this committee (Approval date: March 30, 2023 and protocol code: 2023/027).
Teşekkür
This article is a part of the unpublished PhD thesis of Hakan Serin at Selcuk University, Institute of Health Sciences, Program of Veterinary Biostatistics.
Kaynakça
- Appleby RB, Basran PS, 2022. Artificial intelligence in veterinary medicine. J Am Vet Med Assoc, 260(8): 819-824.
- Aria M, Cuccurullo C, 2017. bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr, 11(4): 959-975.
- Banzato T, Wodzinski M, Burti S, Osti VL, Rossoni V, Atzori M, Zotti A, 2021. Automatic classification of canine thoracic radiographs using deep learning. Sci Rep, 11(1): 3964.
- Bornmann L, Daniel HD, 2007. What do we know about the h index? JASIST, 58(9): 1381-1385.
- Bornmann L, Mutz R, 2015. Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references. J Assoc Inf Sci Technol, 66(11): 2215-2222.
- Bouchemla F, Akchurin SV, Akchurina IV, Dyulger GP, Latynina ES, Grecheneva AV, 2023. Artificial intelligence feasibility in veterinary medicine: A systematic review. Vet World, 16(10): 2143-2149.
- Boyack KW, Klavans R, Sorensen AA, Ioannidis JPA, 2013. A list of highly influential biomedical researchers, 1996–2011. Eur J Clin Investig, 43(12): 1339-1365.
- Cavero D, Tölle KH, Buxadé C, Krieter J, 2006. Mastitis detection in dairy cows by application of fuzzy logic. Livest Sci, 105(1-3): 207-13.
- Chen ZF, Hsu YHE, Lee JJ, Chou CH, 2022. Bibliometric analysis of veterinary communication education research over the last two decades: Rare yet essential. Vet Sci, 9(6): 256.
- Cui L, Tang W, Deng X, Jiang B, 2023. Farm animal welfare is a field of interest in China: A bibliometric analysis based on CiteSpace. Animals, 13(19): 3143.
- Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM, 2021. How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res, 133: 285-296.
- Ebrahimi M, Mohammadi-Dehcheshmeh M, Ebrahimie E, Petrovski KR, 2019. Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep learning and gradient-boosted trees outperform other models. Comput Biol Med, 114: 103456.
- Egghe L, 2006. Theory and practise of the g-index. Scientometrics, 69(1): 131-152.
- Fırat S, Kurutkan MN, Orhan F, 2018. Sağlık politikası konusunun bilim haritalama teknikleri ile analizi. İksad Yayınevi, Adıyaman, 28-73.
- Garfield E, 1980. Bradford law and related statistical patterns. Curr Contents, (19): 5-12.
- Gray A, Price R, 2020. Using InCites responsibly: A guide to interpretation and good practice.
- Harzing AW, 2012. Reflections on the h-index. Business & Leadership, 1(9): 101-106.
- Kaul V, Enslin S, Gross SA, 2020. History of artificial intelligence in medicine. Gastrointest Endosc, 92(4): 807-812.
- Kour S, Agrawal R, Sharma N, Tikoo A, Pande N, Sawhney A, 2022. Artificial intelligence and its application in animal disease diagnosis. J Anim Res, 12(1): 1-10.
- Merigó JM, Yang JB, 2017. A bibliometric analysis of operations research and management science. Omega, 73: 37-48.
- Muehlematter UJ, Daniore P, Vokinger KN, 2021. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): A comparative analysis. Lancet Digit Health, 3(3): e195-e203.
- Owens A, Vinkemeier D, Elsheikha H, 2023. A review of applications of artificial intelligence in veterinary medicine. Com Anim, 28(6): 78-85.
- Rao Y, Jiang M, Wang W, Zhang W, Wang R, 2020. On-farm welfare monitoring system for goats based on Internet of Things and machine learning. Int J Distrib Sens Netw, 16(7): 1550147720944030.
- Russell SJ, 2010. Artificial Intelligence: A Modern Approach. Pearson Education, Inc., Pearson, London, UK, pp: 23.
- Sudhier KP, 2013. Lotka’s law and pattern of author productivity in the area of physics research. DESIDOC J Libr Inf Technol, 33(6): 457-464.
- Van Eck NJ, Waltman L, 2017. Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111: 1053-1070.
- Wang C, Lim MK, Zhao L, Tseng ML, Chien CF, Lev B, 2020. The evolution of Omega-The International Journal of Management Science over the past 40 years: A bibliometric overview. Omega, 93: 102098.
- Yardibi F, Fırat MZ, Teke EC, 2021. Trend topics in animal science: A bibliometric analysis using CiteSpace. Turk J Vet Anim Sci, 45(5): 833-840.
- Yu Y, Li Y, Zhang Z, Gu Z, Zhong H, Zha Q, Yang L, Zhu C, Chen E, 2020. A bibliometric analysis using VOSviewer of publications on COVID-19. Ann Transl Med, 8(13): 816.
Öz
The use of artificial intelligence in veterinary sciences has placed studies on this subject in a significant position in the literature. The increasing number of studies using artificial intelligence algorithms in the current literature shows that knowledge discovery in this field is increasing rapidly. This study aims to provide a general map of the literature on the utilization of artificial intelligence in veterinary medicine science and identify its application areas using bibliometric analysis. Web of Science database was used to reveal the current literature about artificial intelligence in veterinary medicine. The data were analyzed using the “Bibliometrix” package in the R statistical programming language and the VOSviewer program. Various research elements, including journals, article-citation counts, authors, institutes, and countries, were examined using bibliometric metrics. The number of studies on artificial intelligence in veterinary medicine from increased dramatically since 2019. According to the findings, the most influential countries identified were the USA, China, and Türkiye. Animals and Preventive Veterinary Medicine were determined as the most influential journals in the field. The findings indicated that artificial intelligence in veterinary medicine is a trending topic. The topics “deep learning”, “active learning”, and “computer-aided diagnosis” were estimated to be increasingly utilized soon. Rapid developments in artificial intelligence will likely attract more researchers to the field. This article, the first bibliometric study about the utilization of artificial intelligence in animal sciences, will offer researchers valuable information about the intellectual structure of the field.
Anahtar Kelimeler
Artificial intelligence Bibliometric analysis Classification problem Machine learning Veterinary sciences
Etik Beyan
Ethics committee permissions for this study were obtained from Selçuk University Faculty of Veterinary Medicine, Experimental Animal Production and Research Centre Ethics Committee and the study was carried out within the scope of the permission of this committee (Approval date: March 30, 2023 and protocol code: 2023/027).
Teşekkür
This article is a part of the unpublished PhD thesis of Hakan Serin at Selcuk University, Institute of Health Sciences, Program of Veterinary Biostatistics.
Kaynakça
- Appleby RB, Basran PS, 2022. Artificial intelligence in veterinary medicine. J Am Vet Med Assoc, 260(8): 819-824.
- Aria M, Cuccurullo C, 2017. bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr, 11(4): 959-975.
- Banzato T, Wodzinski M, Burti S, Osti VL, Rossoni V, Atzori M, Zotti A, 2021. Automatic classification of canine thoracic radiographs using deep learning. Sci Rep, 11(1): 3964.
- Bornmann L, Daniel HD, 2007. What do we know about the h index? JASIST, 58(9): 1381-1385.
- Bornmann L, Mutz R, 2015. Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references. J Assoc Inf Sci Technol, 66(11): 2215-2222.
- Bouchemla F, Akchurin SV, Akchurina IV, Dyulger GP, Latynina ES, Grecheneva AV, 2023. Artificial intelligence feasibility in veterinary medicine: A systematic review. Vet World, 16(10): 2143-2149.
- Boyack KW, Klavans R, Sorensen AA, Ioannidis JPA, 2013. A list of highly influential biomedical researchers, 1996–2011. Eur J Clin Investig, 43(12): 1339-1365.
- Cavero D, Tölle KH, Buxadé C, Krieter J, 2006. Mastitis detection in dairy cows by application of fuzzy logic. Livest Sci, 105(1-3): 207-13.
- Chen ZF, Hsu YHE, Lee JJ, Chou CH, 2022. Bibliometric analysis of veterinary communication education research over the last two decades: Rare yet essential. Vet Sci, 9(6): 256.
- Cui L, Tang W, Deng X, Jiang B, 2023. Farm animal welfare is a field of interest in China: A bibliometric analysis based on CiteSpace. Animals, 13(19): 3143.
- Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM, 2021. How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res, 133: 285-296.
- Ebrahimi M, Mohammadi-Dehcheshmeh M, Ebrahimie E, Petrovski KR, 2019. Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep learning and gradient-boosted trees outperform other models. Comput Biol Med, 114: 103456.
- Egghe L, 2006. Theory and practise of the g-index. Scientometrics, 69(1): 131-152.
- Fırat S, Kurutkan MN, Orhan F, 2018. Sağlık politikası konusunun bilim haritalama teknikleri ile analizi. İksad Yayınevi, Adıyaman, 28-73.
- Garfield E, 1980. Bradford law and related statistical patterns. Curr Contents, (19): 5-12.
- Gray A, Price R, 2020. Using InCites responsibly: A guide to interpretation and good practice.
- Harzing AW, 2012. Reflections on the h-index. Business & Leadership, 1(9): 101-106.
- Kaul V, Enslin S, Gross SA, 2020. History of artificial intelligence in medicine. Gastrointest Endosc, 92(4): 807-812.
- Kour S, Agrawal R, Sharma N, Tikoo A, Pande N, Sawhney A, 2022. Artificial intelligence and its application in animal disease diagnosis. J Anim Res, 12(1): 1-10.
- Merigó JM, Yang JB, 2017. A bibliometric analysis of operations research and management science. Omega, 73: 37-48.
- Muehlematter UJ, Daniore P, Vokinger KN, 2021. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): A comparative analysis. Lancet Digit Health, 3(3): e195-e203.
- Owens A, Vinkemeier D, Elsheikha H, 2023. A review of applications of artificial intelligence in veterinary medicine. Com Anim, 28(6): 78-85.
- Rao Y, Jiang M, Wang W, Zhang W, Wang R, 2020. On-farm welfare monitoring system for goats based on Internet of Things and machine learning. Int J Distrib Sens Netw, 16(7): 1550147720944030.
- Russell SJ, 2010. Artificial Intelligence: A Modern Approach. Pearson Education, Inc., Pearson, London, UK, pp: 23.
- Sudhier KP, 2013. Lotka’s law and pattern of author productivity in the area of physics research. DESIDOC J Libr Inf Technol, 33(6): 457-464.
- Van Eck NJ, Waltman L, 2017. Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111: 1053-1070.
- Wang C, Lim MK, Zhao L, Tseng ML, Chien CF, Lev B, 2020. The evolution of Omega-The International Journal of Management Science over the past 40 years: A bibliometric overview. Omega, 93: 102098.
- Yardibi F, Fırat MZ, Teke EC, 2021. Trend topics in animal science: A bibliometric analysis using CiteSpace. Turk J Vet Anim Sci, 45(5): 833-840.
- Yu Y, Li Y, Zhang Z, Gu Z, Zhong H, Zha Q, Yang L, Zhu C, Chen E, 2020. A bibliometric analysis using VOSviewer of publications on COVID-19. Ann Transl Med, 8(13): 816.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Hayvansal Üretim (Diğer) |
| Bölüm | Research Articles |
| Yazarlar | |
| Yayımlanma Tarihi | 15 Mayıs 2025 |
| Gönderilme Tarihi | 24 Şubat 2025 |
| Kabul Tarihi | 2 Nisan 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 8 Sayı: 3 |
