Comparison of radiological interpretation made by veterinary radiologists and state-of-the-art commercial AI software for canine and feline radiographic studies.

Introduction: As human medical diagnostic expertise is scarcely available, especially in veterinary care, artificial intelligence (AI) has been increasingly used as a remedy. AI's promise comes from improving human diagnostics or providing good diagnostics at lower cost, increasing access. This study analyzed the diagnostic performance of a widely used AI radiology software vs. veterinary radiologists in interpreting canine and feline radiographs. We aimed to establish whether the performance of commonly used AI matches the performance of a typical radiologist and thus can be reliably used. Secondly, we try to identify in which cases AI is effective. Methods: Fifty canine and feline radiographic studies in DICOM format were anonymized and reported by 11 board-certified veterinary radiologists (ECVDI or ACVR) and processed with commercial and widely used AI software dedicated to small animal radiography (SignalRAY Results: AI matched the best radiologist in accuracy and was more specific but less sensitive than human radiologists. AI did better than the median radiologist overall in low- and high-ambiguity cases. In high-ambiguity cases, AI's accuracy remained high, though it was less effective at detecting abnormalities but better at identifying normal findings. The study confirmed AI's reliability, especially in low-ambiguity scenarios. Conclusion: 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.