Manual CT protocol selection persists as a time-intensive and error-prone bottleneck in radiology workflows, impeding the realization of fully automated scanning pipelines. To overcome this limitation, we developed a Large Language Model Retrieval-Augmented Generation (LLM-RAG) framework for personalized CT protocol recommendation. This system constructs a protocol knowledge base from historical examination records to deliver institutionally tailored, precision recommendations aligned with clinical preferences. Our system demonstrated compelling performance (min: 88.60% precision, 89.34% recall, 88.08% F1, 96.09% accuracy), with key findings revealing: (1) task-specific parity between Qwen and DeepSeek models at equivalent scales (max Δ = 1.41% at 32B); (2) positive scaling laws where larger models boost accuracy (e.g., DeepSeek 7B → 32B: + 1.55%); and (3) linear GPU memory-cost scaling (7B:25 GB → 32B:95 GB), defining clinical deployment constraints. Error analysis of 225 discordant cases identified three primary patterns: over-recommendation (52.44%), unsuitable recommendation (27.56%), and clinically equivalent choices (20%). Critically, the framework achieves clinically viable accuracy without model retraining requirements—a pivotal advantage enabling significant utility in streamlining scanning operations and accelerating imaging workflow automation.
Meng et al. (Thu,) studied this question.