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An online platform for interactive feedback in biomedical machine learning

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Machine learning models have great potential in biomedical applications. A new platform called GradioHub offers an interactive and intuitive way for clinicians and biomedical researchers to try out models and test their reliability on real-world, out-of-training data.

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Fig. 1: GradioHub workflow and example usage.

Code availability

Users can interact with curated models on GradioHub at https://www.gradiohub.com. The code for the GradioHub Python library is available at https://github.com/gradio-app/gradio-UI, and an additional tutorial on creating interactive interfaces with GradioHub is provided at https://www.gradiohub.com/getting_started.html.

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Acknowledgements

We would like to thank D. Ouyang, A. Ghorbani and A. Pampari for feedback. J.Z. is supported by the National Science Foundation grant CCF 1763191, and National Institutes of Health grants R21 MD012867-01 and P30AG059307, and grants from the Silicon Valley Foundation and the Chan Zuckerberg Initiative.

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Authors and Affiliations

  1. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

    Abubakar Abid & James Zou

  2. Gradio Labs, Mountain View, CA, USA

    Abubakar Abid, Ali Abdalla, Ali Abid, Dawood Khan & Abdulrahman Alfozan

  3. Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

    James Zou

Authors
  1. Abubakar Abid
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  2. Ali Abdalla
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  3. Ali Abid
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  4. Dawood Khan
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  5. Abdulrahman Alfozan
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  6. James Zou
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Corresponding authors

Correspondence to Abubakar Abid or James Zou.

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The first five authors are affiliated with Gradio Labs.

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Abid, A., Abdalla, A., Abid, A. et al. An online platform for interactive feedback in biomedical machine learning. Nat Mach Intell 2, 86–88 (2020). https://doi.org/10.1038/s42256-020-0147-8

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