Paraphrases express the same intent in different words, so a robust vision-language model (VLM) should preserve its clinical conclusion when a question is rephrased. Medical VLMs often violate this expectation. We audit this failure on VQA-RAD using nine clinically grounded linguistic operations and 9,723 answer-preserving paraphrases. Across eight checkpoints from five architecture stacks (2.2B–27.4B), Negation Reframing is consistently the most fragile category, with clinical flip rates from 30.7% to 95.3%. A locked, category-conditioned LoRA reduces this failure by 59.0–75.8% across all eight checkpoints; on MedGemma-27B, paraphrase accuracy rises from 68.4% to 75.5%, although base accuracy falls from 79.6% to 73.9%. A pooled adapter performs nearly as well, so we use category-conditioned adapters primarily as diagnostic instruments. Two clinicians agree on 97.8% of a 500-pair calibration sample (κ=0.901), whereas agreement with an LLM equivalence judge is only κ≈0.14. Sparse-autoencoder clamping yields only a 5.8% reduction, and activation patching provides suggestive rather than conclusive localization evidence. Together, these results provide a reproducible robustness audit for radiology VLMs and make its dataset, generator, evaluation, and deployment limits explicit.

This study presents efforts focused on extracting and structuring doctor notes, specifically Magnetic Resonance Imaging (MRI) reports, into a standardized format using large language models (LLMs). We introduce a novel benchmark dataset comprising of 55 clinically relevant variables given by doctors, making it the first of its kind in the automated processing of unstructured medical texts. The annotations to the dataset were generated using a systematic prompt-tuning approach that was manually validated. It was then evaluated across three experimental stages: baseline, intermediate, and fine-tuned. Each stage assessed the impact of different prompt strategies on the performance of various LLMs (LLaMA, Qwen, and DeepSeek). Among the models tested, LLaMA 3.1 8B Instruct consistently achieved the highest composite Score in both the intermediate and final phases, resulting in an 18.42% improvement in performance.