@inproceedings{liu-etal-2025-medical,
    title = "Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?",
    author = "Liu, Che  and
      Wan, Zhongwei  and
      Wang, Haozhe  and
      Chen, Yinda  and
      Qaiser, Talha  and
      Jin, Chen  and
      Burlutskiy, Nikolay  and
      Yousefi, Fariba  and
      Arcucci, Rossella",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.843/",
    pages = "16401--16421",
    ISBN = "979-8-89176-256-5",
    abstract = "Medical Vision-Language Pre-training (MedVLP) has made significant progress in enabling zero-shot tasks for medical image understanding. However, training MedVLP models typically requires large-scale datasets with paired, high-quality image-text data, which are scarce in the medical domain. Recent advancements in Large Language Models (LLMs) and diffusion models have made it possible to generate large-scale synthetic image-text pairs. This raises the question: Can MedVLP succeed using purely synthetic data? To address this, we use off-the-shelf generative models to create synthetic radiology reports and paired Chest X-ray (CXR) images, and propose an automated pipeline to build a diverse, high-quality synthetic dataset, enabling a rigorous study that isolates model and training settings, focusing entirely from the data perspective.Our results show that MedVLP models trained exclusively on synthetic data outperform those trained on real data by 3.8{\%} in averaged AUC on zero-shot classification. Moreover, using a combination of synthetic and real data leads to a further improvement of 9.07{\%}. Additionally, MedVLP models trained on synthetic or mixed data consistently outperform those trained on real data in zero-shot grounding, as well as in fine-tuned classification and segmentation tasks.Our analysis suggests MedVLP trained on well-designed synthetic data can outperform models trained on real datasets, which may be limited by low-quality samples and long-tailed distributions[{\textasciicircum}1].[{\textasciicircum}1]: All data and code will be released upon acceptance."
}