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Explainable AI for Healthcare: From Black Box to Interpretable Models

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Embedded Systems and Artificial Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1076))

Abstract

As artificial intelligence penetrates deeper into work and personal life, it raises questions about trust and transparency. These questions are of greater consequence in healthcare where decisions are literally a matter of life and death. In this paper, we reflect on recent investigations about the interpretability and explainability of artificial intelligence methods and discuss their impact on medicine and healthcare.

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Author information

Authors and Affiliations

  1. Computer and Interdisciplinary Physics Laboratory (LIPI), ENS Fez Sidi Mohammed Ben Abdellah University, Fez, Morocco

    Amina Adadi & Mohammed Berrada

Authors
  1. Amina Adadi
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  2. Mohammed Berrada
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Corresponding author

Correspondence to Amina Adadi .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  3. Department of Computer Sciences, Faculty of Sciences Dhar Mahraz, Sidi Mohammed Ben Abbdallah University, Fez, Morocco

    Hassan Satori

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Adadi, A., Berrada, M. (2020). Explainable AI for Healthcare: From Black Box to Interpretable Models. In: Bhateja, V., Satapathy, S., Satori, H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0947-6_31

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