Translating AI to Clinical Practice: Overcoming Data Shift with Explainability.

Journal: Radiographics : a review publication of the Radiological Society of North America, Inc
Published Date: May 1, 2023

Abstract

To translate artificial intelligence (AI) algorithms into clinical practice requires generalizability of models to real-world data. One of the main obstacles to generalizability is data shift, a data distribution mismatch between model training and real environments. Explainable AI techniques offer tools to detect and mitigate the data shift problem and develop reliable AI for clinical practice. Most medical AI is trained with datasets gathered from limited environments, such as restricted disease populations and center-dependent acquisition conditions. The data shift that commonly exists in the limited training set often causes a significant performance decrease in the deployment environment. To develop a medical application, it is important to detect potential data shift and its impact on clinical translation. During AI training stages, from premodel analysis to in-model and post hoc explanations, explainability can play a key role in detecting model susceptibility to data shift, which is otherwise hidden because the test data have the same biased distribution as the training data. Performance-based model assessments cannot effectively distinguish the model overfitting to training data bias without enriched test sets from external environments. In the absence of such external data, explainability techniques can aid in translating AI to clinical practice as a tool to detect and mitigate potential failures due to data shift. RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Authors

  • Youngwon Choi
    Department of Biostatistics, University of California, Los Angeles, California, USA.
  • Wenxi Yu
    Department of Biostatistics, University of California, Los Angeles, California, USA.
  • Mahesh B Nagarajan
    Department of Radiological Sciences, University of California Los Angeles, Los Angeles, USA.
  • Pangyu Teng
    Department of Biostatistics, University of California, Los Angeles, California, USA.
  • Jonathan G Goldin
    Department of Radiological Sciences, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine at UCLA, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024.
  • Steven S Raman
    Department of Radiologic Sciences David Geffen School of Medicine, University of California Los Angeles CA.
  • Dieter R Enzmann
    Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
  • Grace Hyun J Kim
    Department of Biostatistics, University of California, Los Angeles, California, USA.
  • Matthew S Brown
    University of California Los Angeles David Geffen School of Medicine, Los Angeles, California, USA.

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