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Machine learning

Synthetic chest X-ray images from text prompts

Nature Biomedical Engineering volume 9, pages 439–440 (2025)Cite this article

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A latent diffusion model pre-trained on pairs of natural images and text descriptors can be adapted to generate realistic chest radiographs that are controlled by free-form medical text prompts.

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Fig. 1: Interacting with an image generative model in plain language.

References

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Authors and Affiliations

  1. Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany

    Daniel Truhn

  2. Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany

    Jakob Nikolas Kather

  3. Department of Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany

    Jakob Nikolas Kather

  4. Department of Medicine I, University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany

    Jakob Nikolas Kather

Authors
  1. Daniel Truhn
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  2. Jakob Nikolas Kather
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Corresponding author

Correspondence to Daniel Truhn.

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Competing interests

J.N.K. declares consulting services for Bioptimus, France; Owkin, France; DoMore Diagnostics, Norway; Panakeia, UK; AstraZeneca, UK; Scailyte, Switzerland; Mindpeak, Germany; and MultiplexDx, Slovakia. Furthermore, J.N.K. holds shares in StratifAI GmbH and Synagen GmbH, Germany, has received a research grant by GSK, and has received honoraria by AstraZeneca, Bayer, Daiichi Sankyo, Eisai, Janssen, MSD, BMS, Roche, Pfizer and Fresenius. D.T. declares honoraria by Bayer and GE, and holds shares in StratifAI GmbH and Synagen GmbH, Germany.

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Truhn, D., Kather, J.N. Synthetic chest X-ray images from text prompts. Nat. Biomed. Eng 9, 439–440 (2025). https://doi.org/10.1038/s41551-024-01261-z

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