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Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare

Nature Machine Intelligence volume 5, pages 824–829 (2023)Cite this article

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Abstract

Applications of machine learning are becoming increasingly common in medicine and healthcare, enabling more accurate predictive models. However, this often comes at the cost of interpretability, limiting the clinical impact of machine learning methods. To realize the potential of machine learning in healthcare, it is critical to understand such models from the perspective of multiple stakeholders and various angles, necessitating different types of explanation. In this Perspective, we explore five fundamentally different types of post-hoc machine learning interpretability. We highlight the different types of information that they provide, and describe when each can be useful. We examine the various stakeholders in healthcare, delving into their specific objectives, requirements and goals. We discuss how current notions of interpretability can help meet these and what is required for each stakeholder to make machine learning models clinically impactful. Finally, to facilitate adoption, we release an open-source interpretability library containing implementations of the different types of interpretability, including tools for visualizing and exploring the explanations.

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The open-source software package is available via Github at https://github.com/vanderschaarlab/Interpretability.

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Acknowledgements

F.I. and M.v.d.S. are supported by the National Science Foundation (NSF) under grant number 1722516. M.vdS. is also supported by the Office of Naval Research (ONR).

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA

    Fergus Imrie

  2. Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK

    Robert Davis & Mihaela van der Schaar

  3. The Alan Turing Institute, London, UK

    Mihaela van der Schaar

Authors
  1. Fergus Imrie
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Contributions

F.I. and M.v.d.S. conceptualized the manuscript. R.D. developed the interpretability package. F.I. wrote the original draft of the manuscript, and all authors contributed to editing and revising it.

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Correspondence to Fergus Imrie.

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Nature Machine Intelligence thanks Victor Volovici, Po-Hsuan Cameron Chen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Imrie, F., Davis, R. & van der Schaar, M. Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare. Nat Mach Intell 5, 824–829 (2023). https://doi.org/10.1038/s42256-023-00698-2

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