This research demonstrates that entirely synthetic medical image-text data can effectively train vision-language models for radiology applications, potentially solving healthcare's data scarcity problem.

• Synthetic data generated by LLMs and diffusion models achieved 95.6% performance of models trained on real data
• Hybrid approach combining synthetic and real data outperformed models trained only on real data
• Synthetic data provided better zero-shot capabilities for disease diagnosis
• Effective synthetic data requires careful clinical quality control for both images and text

This breakthrough addresses critical challenges in medical AI development by reducing dependence on sensitive patient data while potentially improving diagnostic capabilities across healthcare settings.

Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?