RapidRead: Global Deployment of State-of-the-art Radiology AI for a Large Veterinary Teleradiology Practice
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 12 of its 45 references that are in the map. First posted on arXiv 9 Nov 2021.
Builds on
12 of 45 references.
References · 12
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Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1.
3 papers.
- 2022Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imagingcited by 32
- 2023StudyFormer : Attention-Based and Dynamic Multi View Classifier for X-ray imagescited by 1
- 2025VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imagingcited by 1
- 2025VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imaging21 May 2025
- 2023StudyFormer : Attention-Based and Dynamic Multi View Classifier for X-ray images23 Feb 2023
- 2022Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging19 May 2022