ArticleVeterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association2026
Agreement of Multimodal Large Language Models and Novice Readers With Expert-Based Radiographic Scoring of Presumptive Septic Carpal Arthritis in Calves.
Article in Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Septic arthritis is an important cause of morbidity in calves, and radiographic interpretation may vary between observers, especially when structured scoring systems are used by less experienced readers. This single-center retrospective reader study evaluated whether general-purpose multimodal large language models (LLMs) could support rubric-based radiographic scoring of presumptive septic carpal arthritis in calves. Fifty calves aged 0-3 months with clinical findings consistent with presumptive septic carpal arthritis were included, and one carpal radiograph per case was assessed. Two novice veterinary surgeons and three multimodal LLMs, ChatGPT-5, Gemini-2.5 Pro, and Claude Sonnet-4, independently scored each case using a Constant-based ordinal radiographic scoring framework. Expert consensus served as the operational reference standard. Agreement with the reference was assessed using exact agreement, agreement within one score category, and quadratic weighted kappa. Novice 1 showed the highest concordance, with a mean exact agreement of 55.6%, mean ±1 agreement of 89.8%, and substantial agreement (mean κ
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