Exploring the landscape of explainable artificial intelligence (XAI) : A systematic review of techniques and applications

Journal article

Hamida, Sayda Umma, Chowdhury, Mohammad Jabed Morshed, Chakraborty, Narayan Ranjan, Biswas, Kamanashis and Sami, Shahrab Khan. (2024). Exploring the landscape of explainable artificial intelligence (XAI) : A systematic review of techniques and applications. Big Data and Cognitive Computing. 8(11), p. Article 149. https://doi.org/10.3390/bdcc8110149
AuthorsHamida, Sayda Umma, Chowdhury, Mohammad Jabed Morshed, Chakraborty, Narayan Ranjan, Biswas, Kamanashis and Sami, Shahrab Khan
Abstract

Artificial intelligence (AI) encompasses the development of systems that perform tasks typically requiring human intelligence, such as reasoning and learning. Despite its widespread use, AI often raises trust issues due to the opacity of its decision-making processes. This challenge has led to the development of explainable artificial intelligence (XAI), which aims to enhance user understanding and trust by providing clear explanations of AI decisions and processes. This paper reviews existing XAI research, focusing on its application in the healthcare sector, particularly in medical and medicinal contexts. Our analysis is organized around key properties of XAI—understandability, comprehensibility, transparency, interpretability, and explainability—providing a comprehensive overview of XAI techniques and their practical implications.

Keywordsartificial intelligence; explainable AI; trust in AI; healthcare AI; AI interpretability; AI transparency; XAI properties
Year2024
JournalBig Data and Cognitive Computing
Journal citation8 (11), p. Article 149
PublisherMultidisciplinary Digital Publishing Institute (MDPI AG)
ISSN2504-2289
Digital Object Identifier (DOI)https://doi.org/10.3390/bdcc8110149
Scopus EID2-s2.0-85210452441
Open accessPublished as ‘gold’ (paid) open access
Page range1-30
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Open
Output statusPublished
Publication dates
Online31 Oct 2024
Publication process dates
Accepted27 Sep 2024
Deposited12 Jun 2025
Additional information

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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