The emergence of Large Language Models (LLMs) presents unprecedented opportunities to revolutionize medical contrastive vision-language pre-training. In this paper, we show how LLMs can facilitate large-scale supervised pre-training, thereby advancing vision-language alignment. We begin by demonstrating that modern LLMs can automatically extract diagnostic labels from radiology reports with remarkable precision (>96% AUC in our experiments) without complex prompt engineering, enabling the creation of large-scale “silver-standard” datasets at a minimal cost ( $3 for 50k CT image-report pairs). Further, we find that vision encoders trained on this “silver-standard” dataset achieve performance comparable to those trained on labels extracted by specialized BERT-based models, thereby democratizing the access to large-scale supervised pre-training. Building on this foundation, we proceed to reveal that supervised pre-training fundamentally improves contrastive vision-language alignment. Our approach achieves state-of-the-art performance using only a 3D ResNet-18 with vanilla CLIP training, including 83.8% AUC for zero-shot diagnosis on CT-RATE, 77.3% AUC on RAD-ChestCT, and substantial improvements in cross-modal retrieval (MAP@50=53.7% for image-image, Recall@100=52.2% for report-image). These results demonstrate the potential of utilizing LLMs to facilitate more performant and scalable medical AI systems. Our code is available at https://github.com/SigmaLDC/More-performant-and-scalable .