AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Three flagship OpenAI deployments achieved cartilage grading agreement indistinguishable from human inter-rater variability, while cost-optimized and open-weight variants performed measurably below.
It is suggested that LLMs may support report-based spine imaging diagnosis as clinical assistive tools, but deployment should account for disease prevalence, prompt sensitivity, and the need for domain-specific optimisation.
Clinical vignettes were the primary contributor to improving LLM performance on OITE whereas images had minimal effect and closed-source models achieved substantially higher accuracy than open-source models, and all models performed above chance when provided answer options alone.
Publicly available multimodal LLMs showed measurable but heterogeneous performance in grayscale ultrasound-based thyroid nodule classification, but none of the models matched senior radiologist-level performance.
Objective: To develop and evaluate an AI-assisted MRI method for quantitative knee cartilage morphometry in a multicenter phase III knee osteoarthritis trial. Methods: AI pre-segmentation used 3D full-resolution nnU-Net. Version 1.0 used separate femorotibial- and patellar-cartilage models, whereas version 2.0 used a u...
This study provides a benchmark of LLM performance for radiology OSCE-style content generation and evaluation during a specific snapshot of artificial intelligence development (August 2024).
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.
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