Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis
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Published:2023-10-03
Issue:10
Volume:14
Page:541
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ISSN:2078-2489
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Container-title:Information
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language:en
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Short-container-title:Information
Author:
Dhiman Pummy1ORCID, Bonkra Anupam2, Kaur Amandeep1ORCID, Gulzar Yonis3ORCID, Hamid Yasir4ORCID, Mir Mohammad Shuaib3ORCID, Soomro Arjumand Bano35ORCID, Elwasila Osman3
Affiliation:
1. Institute of Engineering and Technology, Chitkara University, Rajpura 140417, Punjab, India 2. Information Technology Department, Chandigarh Group of Colleges, Landran 140307, Punjab, India 3. Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia 4. Information Security and Engineering Technology, Abu Dhabi Polytechnic College, Abu Dhabi 111499, United Arab Emirates 5. Department of Software Engineering, Faculty of Engineering and Technology, University of Sindh, Jamshoro 76080, Pakistan
Abstract
Recent developments in IoT, big data, fog and edge networks, and AI technologies have had a profound impact on a number of industries, including medical. The use of AI for therapeutic purposes has been hampered by its inexplicability. Explainable Artificial Intelligence (XAI), a revolutionary movement, has arisen to solve this constraint. By using decision-making and prediction outputs, XAI seeks to improve the explicability of standard AI models. In this study, we examined global developments in empirical XAI research in the medical field. The bibliometric analysis tools VOSviewer and Biblioshiny were used to examine 171 open access publications from the Scopus database (2019–2022). Our findings point to several prospects for growth in this area, notably in areas of medicine like diagnostic imaging. With 109 research articles using XAI for healthcare classification, prediction, and diagnosis, the USA leads the world in research output. With 88 citations, IEEE Access has the greatest number of publications of all the journals. Our extensive survey covers a range of XAI applications in healthcare, such as diagnosis, therapy, prevention, and palliation, and offers helpful insights for researchers who are interested in this field. This report provides a direction for future healthcare industry research endeavors.
Funder
Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia
Subject
Information Systems
Reference58 articles.
1. Bonkra, A., and Dhiman, P. (2021, January 17–18). IoT Security Challenges in Cloud Environment. Proceedings of the 2021 2nd International Conference on Computational Methods in Science & Technology, Mohali, India. 2. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI;Bennetot;Inf. Fusion,2020 3. Van Lent, M., Fisher, W., and Mancuso, M. (2004, January 25–29). An explainable artificial intelligence system for small-unit tactical behavior. Proceedings of the Nineteenth National Conference on Artificial Intelligence, Sixteenth Conference on Innovative Applications of Artificial Intelligence, San Jose, CA, USA. 4. Mukhtar, M., Bilal, M., Rahdar, A., Barani, M., Arshad, R., Behl, T., and Bungau, S. (2020). Nanomaterials for diagnosis and treatment of brain cancer: Recent updates. Chemosensors, 8. 5. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI);Adadi;IEEE Access,2018
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