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A Comprehensive Study of Explainable Artificial Intelligence in Healthcare

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Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1024))

Abstract

The recent development of Artificial intelligence and Machine learning, in general, has exhibited impressive results in a variety of fields, especially through the introduction of deep learning (DL). Even though they show an extraordinary performance in a substantial number of jobs and have tremendous potential. This surge in performance is usually pulled off through the increase in model complexity, giving rise to the black-box model and creating confusion about how they work and, ultimately, how they make judgments. This uncertainty has made it difficult for machine-learning programs to be used in more sensitive but essential areas, such as health care, where their benefits can be enormous, Thus giving birth to the need for Explainable AI. Explainable Artificial Intelligence (XAI) is a new machine-learning research subject aiming at decoding how AI systems make black-box decisions. This chapter focuses on the need for Explainable AI in the field of healthcare and some techniques like LIME, SHAP, PDPs, and a few others, through which complex models can be explained. We will see the use of explainable methods by analyzing two case studies. Through the use of this article, clinicians, theorists, and practitioners can get a better insight into how these models work and can help to bring a high level of accountability and transparency.

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Authors and Affiliations

  1. Kalinga Institute of Industrial Technology, Bhubaneswar, India

    Aryan Mohanty & Sushruta Mishra

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  1. Aryan Mohanty
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  2. Sushruta Mishra
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Corresponding author

Correspondence to Sushruta Mishra .

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Editors and Affiliations

  1. Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

    Sushruta Mishra

  2. Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

    Hrudaya Kumar Tripathy

  3. School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

    Pradeep Mallick

  4. Dubai International Academic City, The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

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Mohanty, A., Mishra, S. (2022). A Comprehensive Study of Explainable Artificial Intelligence in Healthcare. In: Mishra, S., Tripathy, H.K., Mallick, P., Shaalan, K. (eds) Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis. Studies in Computational Intelligence, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-19-1076-0_25

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