Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations

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.