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Harnessing medical twitter data for pathology AI

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Using pathology data from Twitter, researchers have built a visual-language model for classifying and retrieving histopathology images — representing a milestone in the development of multifunctional foundational artificial intelligence models in computational pathology.

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Fig. 1: Zero-shot classification with CLIP and PLIP.

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Authors and Affiliations

  1. Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Ming Y. Lu, Bowen Chen & Faisal Mahmood

  2. Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Ming Y. Lu, Bowen Chen & Faisal Mahmood

  3. Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Ming Y. Lu & Faisal Mahmood

  4. Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA

    Ming Y. Lu

  5. Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA

    Ming Y. Lu

  6. Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA

    Ming Y. Lu & Faisal Mahmood

Authors
  1. Ming Y. Lu
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  2. Bowen Chen
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  3. Faisal Mahmood
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Corresponding author

Correspondence to Faisal Mahmood.

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The authors declare no competing interests.

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Lu, M.Y., Chen, B. & Mahmood, F. Harnessing medical twitter data for pathology AI. Nat Med 29, 2181–2182 (2023). https://doi.org/10.1038/s41591-023-02530-1

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