TL;DR
Although ChatGPT-5.0 outperformed ChatGPT-4o, both models remained inferior to clinicians in detailed KL grading, feature-level interpretation, and reproducibility and are not suitable for standalone radiographic KOA assessment.
Three flagship OpenAI deployments achieved cartilage grading agreement indistinguishable from human inter-rater variability, while cost-optimized and open-weight variants performed measurably below.
ChatGPT-5 outperformed ChatGPT-4o in detecting lumbar spondylolisthesis and both LLMs remained limited compared to fellowship trained spine surgeons.
ChatGPT-5 showed moderate-to-substantial agreement with expert consensus under standardized experimental conditions under curated synthetic dataset, however, the curated synthetic dataset, limited technical reproducibility of web-based inference, systematic grading tendencies, exploratory nature of the secondary-model...
Off-the-shelf multimodal LLMs approximate human performance for binary surgical indication but remain inferior for precise level localization, establish a practice-relevant baseline of spatial reasoning limitations for tools already used by patients and junior doctors.
The findings support the potential role of MLLMs as assistive tools in human-AI orthopedic imaging workflows, but external validation, careful input standardization, and prospective clinical evaluation are needed before clinical deployment.
Objective To evaluate the diagnostic performance of multiple large language models (LLMs) against expert consensus in determining surgical intervention needs for feline metacarpal and metatarsal fractures. Methods In this retrospective study (December 2023 to February 2025), 73 clinical cases of feline metacarpal and...
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