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Volume 10 Issue 4
Apr.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  >  10(4): 859-876
A. Chaddad, Q. Z. Lu, J. L. Li, Y. Katib, R. Kateb, C. Tanougast, A. Bouridane, and A. Abdulkadir, “Explainable, domain-adaptive, and federated artificial intelligence in medicine,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 859–876, Apr. 2023. doi: 10.1109/JAS.2023.123123
Citation: A. Chaddad, Q. Z. Lu, J. L. Li, Y. Katib, R. Kateb, C. Tanougast, A. Bouridane, and A. Abdulkadir, “Explainable, domain-adaptive, and federated artificial intelligence in medicine,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 859–876, Apr. 2023. doi: 10.1109/JAS.2023.123123
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Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

doi: 10.1109/JAS.2023.123123
  • 1.

    School of Artificial intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China. A. Chaddad is also with The Laboratory for Imagery, Vision and Artificial Intelligence, Ecole de Technologie Superieure, Montreal, Quebec H3C 1K3, Canada

  • 2.

    College of Medicine, Taibah University, Madinah 42353, Saudi Arabia

  • 3.

    College of Computer Science and Engineering, Taibah University, Madinah 42353, Saudi Arabia

  • 4.

    Laboratory of Design, Optimization and Modeling Systems, University of Lorraine, Metz 57070, France

  • 5.

    Center for Data Analytics and Cybersecurity (CDAC), University of Sharjah, Sharjah 27272, United Arab Emirates

  • 6.

    Laboratoire de recherche en neuroimagerie, Centre Hospitalier Universitaire Vaudois, Mont Paisible 16, 1011, Lausanne, Switzerland

Funds:  This work was supported in part by the National Natural Science Foundation of China (82260360) and the Foreign Young Talent Program (QN2021033002L)
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    Abstract
  • Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in each domain is driven by a growing body of annotated data, increased computational resources, and technological innovations. In medicine, the sensitivity of the data, the complexity of the tasks, the potentially high stakes, and a requirement of accountability give rise to a particular set of challenges. In this review, we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making. 1) Explainable AI aims to produce a human-interpretable justification for each output. Such models increase confidence if the results appear plausible and match the clinicians expectations. However, the absence of a plausible explanation does not imply an inaccurate model. Especially in highly non-linear, complex models that are tuned to maximize accuracy, such interpretable representations only reflect a small portion of the justification. 2) Domain adaptation and transfer learning enable AI models to be trained and applied across multiple domains. For example, a classification task based on images acquired on different acquisition hardware. 3) Federated learning enables learning large-scale models without exposing sensitive personal health information. Unlike centralized AI learning, where the centralized learning machine has access to the entire training data, the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates, not personal health data. This narrative review covers the basic concepts, highlights relevant corner-stone and state-of-the-art research in the field, and discusses perspectives.

     

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    • We provide a comprehensive review of explainable artificial intelligence (XAI), federated learning (FL), and domain adaptation (DA) in the context of applications to medical data
    • We describe XAI, FL, and DA methods and their applications in medicine. Persisting challenges, and future research directions of these three techniques, are also discussed
    • We provide tabulated overview of methods and their applications with references to open-source algorithms and data sets

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