Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations
Тип публикации: Journal Article
Дата публикации: 2024-08-01
scimago Q1
wos Q2
БС1
SJR: 0.608
CiteScore: 4.6
Impact factor: 1.8
ISSN: 00345288, 15322661
PubMed ID:
38843690
Краткое описание
The field of veterinary diagnostic imaging is undergoing significant transformation with the integration of artificial intelligence (AI) tools. This manuscript provides an overview of the current state and future prospects of AI in veterinary diagnostic imaging. The manuscript delves into various applications of AI across different imaging modalities, such as radiology, ultrasound, computed tomography, and magnetic resonance imaging. Examples of AI applications in each modality are provided, ranging from orthopaedics to internal medicine, cardiology, and more. Notable studies are discussed, demonstrating AI's potential for improved accuracy in detecting and classifying various abnormalities. The ethical considerations of using AI in veterinary diagnostics are also explored, highlighting the need for transparent AI development, accurate training data, awareness of the limitations of AI models, and the importance of maintaining human expertise in the decision-making process. The manuscript underscores the significance of AI as a decision support tool rather than a replacement for human judgement. In conclusion, this comprehensive manuscript offers an assessment of the current landscape and future potential of AI in veterinary diagnostic imaging. It provides insights into the benefits and challenges of integrating AI into clinical practice while emphasizing the critical role of ethics and human expertise in ensuring the wellbeing of veterinary patients.
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ГОСТ
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Burti S. et al. Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations // Research in Veterinary Science. 2024. Vol. 175. p. 105317.
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Burti S., Banzato T., Coghlan S., Wodzinski M., Bendazzoli M., Zotti A. Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations // Research in Veterinary Science. 2024. Vol. 175. p. 105317.
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TY - JOUR
DO - 10.1016/j.rvsc.2024.105317
UR - https://linkinghub.elsevier.com/retrieve/pii/S0034528824001838
TI - Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations
T2 - Research in Veterinary Science
AU - Burti, Silvia
AU - Banzato, Tommaso
AU - Coghlan, Simon
AU - Wodzinski, Marek
AU - Bendazzoli, Margherita
AU - Zotti, Alessandro
PY - 2024
DA - 2024/08/01
PB - Elsevier
SP - 105317
VL - 175
PMID - 38843690
SN - 0034-5288
SN - 1532-2661
ER -
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BibTex (до 50 авторов)
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@article{2024_Burti,
author = {Silvia Burti and Tommaso Banzato and Simon Coghlan and Marek Wodzinski and Margherita Bendazzoli and Alessandro Zotti},
title = {Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations},
journal = {Research in Veterinary Science},
year = {2024},
volume = {175},
publisher = {Elsevier},
month = {aug},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0034528824001838},
pages = {105317},
doi = {10.1016/j.rvsc.2024.105317}
}