Comparison of radiological interpretation made by veterinary radiologists and state-of-the-art commercial AI software for canine and feline radiographic studies
Our findings suggest that AI performs almost as well as the best veterinary radiologist in all settings of descriptive radiographic findings. However, its strengths lie more in confirming normality than detecting abnormalities, and it does not provide differential diagnoses. Therefore, the broader use of AI could reliably increase diagnostic availability but requires further human input. Given the unique strengths of human experts and AI and the differences in sensitivity vs. specificity and low-ambiguity vs. high-ambiguity settings, AI will likely complement rather than replace human experts.
Authors
- Tobias Schwarz (ORCID: https://orcid.org/0000-0001-8412-573X)
- Axel Ockenfels (ORCID: https://orcid.org/0000-0003-1456-0191)
- Peter Cramton (ORCID: https://orcid.org/0000-0001-8621-1092)
- Chavdar Chernev (ORCID: https://orcid.org/0000-0001-7416-6159)
- Yero S. Ndiaye
Institutions
- University of Maryland, Baltimore (US)
- Roslin Institute (GB)
- University of Cologne (DE)
- University of Veterinary Medicine Hannover, Foundation (DE)
- Max Planck Institute for Behavioral Economics (DE)
- University of Edinburgh (GB)
Publication Details
- Journal
- Frontiers in Veterinary Science
- Published
- 2025-02-21
- DOI
- https://doi.org/10.3389/fvets.2025.1502790
- Citations
- 9
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 12.24
Funders
- Deutsche Forschungsgemeinschaft