RapidRead: Global Deployment of State-of-the-art Radiology AI for a Large Veterinary Teleradiology Practice
This addresses the need for efficient and scalable diagnostic tools in veterinary teleradiology, though it appears incremental as it applies existing AI methods to a new domain.
The authors tackled the problem of automating radiology assessments for veterinary practices by developing and deploying a deep learning AI system for canine and feline radiographs, achieving real-time performance with over 2.5 million images used in training.
This work describes the development and real-world deployment of a deep learning-based AI system for evaluating canine and feline radiographs across a broad range of findings and abnormalities. We describe a new semi-supervised learning approach that combines NLP-derived labels with self-supervised training leveraging more than 2.5 million x-ray images. Finally we describe the clinical deployment of the model including system architecture, real-time performance evaluation and data drift detection.