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Rise of Robot Radiologists

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  • Sara Reardon

Abstract

Deep-learning algorithms are peering into MRIs and x-rays with unmatched vision, but who is to blame when they make a mistake?

Suggested Citation

  • Sara Reardon, 2019. "Rise of Robot Radiologists," Nature, Nature, vol. 576(7787), pages 54-58, December.
    • Handle: RePEc:nat:nature:v:576:y:2019:i:7787:d:10.1038_d41586-019-03847-z
    DOI: 10.1038/d41586-019-03847-z
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    Cited by:

    1. Subhadeep Mukhopadhyay, 2021. "InfoGram and Admissible Machine Learning," Papers 2108.07380, arXiv.org, revised Aug 2021.
    2. Avni Malik & Paranjay Patel & Lubaina Ehsan & Shan Guleria & Thomas Hartka & Sodiq Adewole & Sana Syed, 2021. "Ten simple rules for engaging with artificial intelligence in biomedicine," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-11, February.
    3. Lechardoy, Lucie & López Forés, Laura & Codagnone, Cristiano, 2023. "Artificial intelligence at the workplace and the impacts on work organisation, working conditions and ethics," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277997, International Telecommunications Society (ITS).

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    Keywords

    Health care; Computer science;

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