Szczegóły publikacji
Opis bibliograficzny
Artificial intelligence in veterinary diagnostic imaging: perspectives and limitations / Silvia Burti, Tommaso Banzato, Simon Coghlan, Marek WODZIŃSKI, Margherita Bendazzoli, Alessandro Zotti // Research in Veterinary Science ; ISSN 0034-5288. — 2024 — vol. 175 art. no. 105317, s. 1-9. — Bibliogr. s. 8-9, Abstr. — Publikacja dostępna online od: 2024-05-31. — M. Wodziński - dod. afiliacja: University of Applied Sciences - Western Switzerland
Autorzy (6)
- Burti Silvia
- Banzato Tommaso
- Coghlan Simon
- AGHWodziński Marek
- Bendazzoli Margherita
- Zotti Alessandro
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 155032 |
|---|---|
| Data dodania do BaDAP | 2024-09-24 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.rvsc.2024.105317 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Research in Veterinary Science |
Abstract
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.
