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AI model transferability in healthcare: a sociotechnical perspective

Nature Machine Intelligence volume 4, pages 807–809 (2022)Cite this article

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To deliver value in healthcare, artificial intelligence and machine learning models must be integrated not only into technology platforms but also into local human and organizational ecosystems and workflows. To realize the promised benefits of applying these models at scale, a roadmap of the challenges and potential solutions to sociotechnical transferability is needed.

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Acknowledgements

The authors receive financial support from the US National Science Foundation (awards no. 1928614 and 2129076) for the submitted work. The funding source had no further role in this study.

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Authors and Affiliations

  1. Department of Management and Organizations, NYU Stern School of Business, New York, NY, USA

    Batia Mishan Wiesenfeld

  2. Department of Population Health, NYU Grossman School of Medicine and Langone Health, New York, NY, USA

    Yin Aphinyanaphongs & Oded Nov

  3. Department of Technology Management, NYU Tandon School of Engineering, Brooklyn, NY, USA

    Oded Nov

Authors
  1. Batia Mishan Wiesenfeld
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  2. Yin Aphinyanaphongs
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  3. Oded Nov
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Contributions

B.M.W.: conceptualization, writing—original draft; Y.A.: writing—review and editing; O.N.: conceptualization, writing—review and editing.

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Correspondence to Batia Mishan Wiesenfeld.

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The authors declare no competing interests.

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Nature Machine Intelligence thanks Parisa Rashidi, Peter Winter, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Wiesenfeld, B.M., Aphinyanaphongs, Y. & Nov, O. AI model transferability in healthcare: a sociotechnical perspective. Nat Mach Intell 4, 807–809 (2022). https://doi.org/10.1038/s42256-022-00544-x

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