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  • Perspective
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Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging

Nature Machine Intelligence volume 3, pages 929–935 (2021)Cite this article

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Abstract

The National Institutes of Health in 2018 identified key focus areas for the future of artificial intelligence in medical imaging, creating a foundational roadmap for research in image acquisition, algorithms, data standardization and translatable clinical decision support systems. Among the key issues raised in the report, data availability, the need for novel computing architectures and explainable artificial intelligence algorithms are still relevant, despite the tremendous progress made over the past few years alone. Furthermore, translational goals of data sharing, validation of performance for regulatory approval, generalizability and mitigation of unintended bias must be accounted for early in the development process. In this Perspective, we explore challenges unique to high-dimensional clinical imaging data, in addition to highlighting some of the technical and ethical considerations involved in developing machine learning systems that better represent the high-dimensional nature of many imaging modalities. Furthermore, we argue that methods that attempt to address explainability, uncertainty and bias should be treated as core components of any clinical machine learning system.

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Fig. 1: Cloud-based collaborative annotation workflows.
Fig. 2: Quantifying uncertainty in machine learning outputs.
Fig. 3: Misleading nature of post-hoc model explanations.

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Acknowledgements

R.S. was supported in part by the American Heart Association Postdoctoral Fellowship Award (grant number 834986).

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Authors and Affiliations

  1. Department of Cardiothoracic Surgery, Stanford University, Palo Alto, CA, USA

    Rohan Shad & William Hiesinger

  2. Department of Statistics, Columbia University, New York, NY, USA

    John P. Cunningham

  3. Department of Cardiovascular Medicine, Genetics, and Biomedical Data Science, Stanford University, Stanford, CA, USA

    Euan A. Ashley

  4. Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, CA, USA

    Euan A. Ashley, Curtis P. Langlotz & William Hiesinger

  5. Department of Radiology and Biomedical Informatics, Stanford University, Stanford, CA, USA

    Curtis P. Langlotz

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Correspondence to William Hiesinger.

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Shad, R., Cunningham, J.P., Ashley, E.A. et al. Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging. Nat Mach Intell 3, 929–935 (2021). https://doi.org/10.1038/s42256-021-00399-8

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