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Explainable AI and Its Applications in Healthcare

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Explainable AI: Foundations, Methodologies and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 232))

Abstract

Due to the lack of high-end graphics or tensor processing units, previously, deep neural networks could not be implemented as state-of-the-art Artificial Intelligence (AI) algorithms. Rather, linear models were preferred, and they were easy to understand and interpret. Things started changing with the advent of more advanced processing units, in the last decade, when the algorithms took on real-world problems. The models began getting bigger and better.

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Authors and Affiliations

  1. Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany

    Arjun Sarkar

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  1. Arjun Sarkar
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  1. Department of Computer Engineering, Sarvajanik College of Engineering and Technology, Surat, Gujarat, India

    Mayuri Mehta

  2. Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, UK

    Vasile Palade

  3. Department of Computer Engineering, Tongmyong University, Busan, Korea (Republic of)

    Indranath Chatterjee

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Sarkar, A. (2023). Explainable AI and Its Applications in Healthcare. In: Mehta, M., Palade , V., Chatterjee, I. (eds) Explainable AI: Foundations, Methodologies and Applications. Intelligent Systems Reference Library, vol 232. Springer, Cham. https://doi.org/10.1007/978-3-031-12807-3_6

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