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Decrypting the Black Boxing of Artificial Intelligence Using Explainable Artificial Intelligence in Smart Healthcare

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Connected e-Health

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1021))

Abstract

Artificial Intelligence (AI) is creating a revolution in the healthcare industry with its recent developments in organized and amorphous data and quick progress in analytic techniques. The usefulness of AI in healthcare is being recognised at the same time as people begin to be concerned with the possible lack of explainability and bias in the models created. This explains the concept of explainable artificial intelligence (XAI), which increases the faith held in a system, thus leading to more widespread use of AI in healthcare. In this chapter, we offer diverse ways of viewing the XAI concepts, understandability and interpretability of explainable AI systems, mainly focussing on the healthcare domain. The intention is to educate healthcare providers on the understandability and interpretability of explainable AI systems. The medical model is the root cause of life, and we should be assured adequate to treat the patient according to its rules. This chapter uses AI explainability as a way to help build trustworthiness in the medical domain and takes a look at the recent developments in the area of explainable AI which encourages creativity, and at times are necessities in practice to raise awareness.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Pandit Deendayal Energy University Gandhinagar, Gandhinagar, Gujrat, India

    Tawseef Ayoub Shaikh

  2. Department of Computer Engineering, Aligarh Muslim University, Uttar Pradesh, Aligarh, India

    Waseem Ahmad Mir

  3. Department of Information Technology, National Institute of Technology, Srinagar, Jammu & Kashmir, India

    Shabir Sofi

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  1. Tawseef Ayoub Shaikh
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  2. Waseem Ahmad Mir
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  3. Shabir Sofi
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Correspondence to Tawseef Ayoub Shaikh .

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Editors and Affiliations

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

    Sushruta Mishra

  2. Grupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain

    Alfonso González-Briones

  3. KIET Group of Institutions, Ghaziabad, Delhi-NCR, India

    Akash Kumar Bhoi

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

    Pradeep Kumar Mallick

  5. Grupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain

    Juan M. Corchado

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Shaikh, T.A., Mir, W.A., Sofi, S. (2022). Decrypting the Black Boxing of Artificial Intelligence Using Explainable Artificial Intelligence in Smart Healthcare. In: Mishra, S., González-Briones, A., Bhoi, A.K., Mallick, P.K., Corchado, J.M. (eds) Connected e-Health. Studies in Computational Intelligence, vol 1021. Springer, Cham. https://doi.org/10.1007/978-3-030-97929-4_3

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