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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
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Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations.

Silvia Burti1, Tommaso Banzato1, Simon Coghlan2

  • 1Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, Legnaro, 35020 Padua, Italy.

Research in Veterinary Science
|June 6, 2024
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Summary

Artificial intelligence (AI) is transforming veterinary diagnostic imaging across modalities. AI enhances diagnostic accuracy but requires ethical development and human oversight for optimal patient care.

Keywords:
Artificial intelligenceConvolutional neural networkDeep learningEthicsMachine learningVeterinary diagnostic imaging

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Area of Science:

  • Veterinary Medicine
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Veterinary diagnostic imaging is rapidly evolving with the integration of artificial intelligence (AI).
  • AI tools are being applied across various imaging modalities, including radiology, ultrasound, CT, and MRI.
  • Applications span multiple veterinary specialties, from orthopedics to internal medicine and cardiology.

Purpose of the Study:

  • To provide a comprehensive overview of the current applications and future potential of AI in veterinary diagnostic imaging.
  • To discuss the benefits and challenges associated with integrating AI into clinical veterinary practice.
  • To explore the ethical considerations crucial for the responsible implementation of AI in veterinary diagnostics.

Main Methods:

  • Review of current literature and notable studies on AI in veterinary diagnostic imaging.
  • Analysis of AI applications across different imaging modalities and veterinary disciplines.
  • Discussion of ethical frameworks, including data accuracy, model limitations, and human expertise.

Main Results:

  • AI demonstrates potential for improved accuracy in detecting and classifying abnormalities in veterinary patients.
  • Studies highlight AI's utility in enhancing diagnostic capabilities across various imaging techniques.
  • Ethical considerations emphasize transparency, data quality, and the indispensable role of human judgment.

Conclusions:

  • AI is a valuable decision support tool in veterinary diagnostic imaging, not a replacement for veterinary professionals.
  • Responsible integration of AI requires careful attention to ethical guidelines and the preservation of human expertise.
  • The future of veterinary diagnostics will likely involve a synergistic approach between AI and skilled veterinary practitioners to ensure patient well-being.