A scoping review of literature on deep learning and symbolic AI-based framework for detecting Covid-19 using computerized tomography scans

Abstract

This scoping review aims to explore various Deep Learning and Symbolic Artificial Intelligence (AI) models that can be integrated into explainable hybrid AI for the purpose of detecting COVID-19 based on Computerized Tomography (CT) scans. We followed the PRISMA-ScR framework as the foundation for our scoping review protocol. Our approach included a thorough search across 13 databases, complemented by an additional random internet search for relevant articles. Due to the voluminous number of articles returned, the search was further narrowed using the keywords: Deep Learning, Symbolic AI and Hybrid AI. These keywords were used because they are more visible in the earmarked literature. A screening of all articles by title was performed to remove duplicates. The final screening process centered on the publication year, ensuring that all considered articles fell within the range of 2019 to 2023, inclusive. Subsequently, abstract or text synthesis was conducted. Our search query retrieved a total of 3,312 potential articles from the thirteen databases, and an additional 12 articles from a random internet search, resulting in a cumulative count of 3,324 identified articles. After the deduplication and screening steps, 260 articles met our inclusion criteria. These articles were categorized based on the year of publication, the type of aim, and the type of AI used. An analysis of the year of publication revealed a linear trend, indicating growth in the field of Hybrid AI. Out of the five aim categories identified, we deliberately excluded articles that lacked a specified aim. It's noteworthy that 3% of the articles focused on the integration of AI models. The low percentage value suggests that the integration aspect is overlooked, thereby transcripting the integration of Deep Learning and symbolic AI into hybrid AI as an area worth exploring. This scoping review gives an overview of how a Deep Learning and Symbolic AI-based framework has been used in the detection of COVID-19 based on CT scans.

Keywords: {'name': 'Deep Learning, Symbolic AI, Hybrid AI, Scoping Review'}

Authors & affiliations

Vengai Musanga
Affiliation not supplied

Colin Chibaya
Affiliation not supplied

Serestina Viriri
Affiliation not supplied

Deposited references

Supplied references, not a list of publications citing this article.

DOI: 10.1186/s40537-021-00444-8

DOI: 10.1016/j.compbiomed.2020.104037

DOI: 10.2139/ssrn.3559477

Bhatia, R. (2017). Understanding the Difference Between Symbolic and Non-Symbolic AI. EnDeep Learningess Origins https://analyticsindiamag.com/understanding-difference-symbolic-ai-non-symbolic-ai/ [Accessed 23 July 2023]

DOI: 10.1097/XEB.0000000000000202

DOI: 10.1016/j.mlwa.2021.100134

Dickson, B. (2019). What is Symbolic Artificial Intelligence?TechTalks https://bdtechtalks.com/2019/11/18/what-is-symbolic-artificial-intelligence/ [Accessed 23 July 2023]

DOI: 10.1109/CVPR.2018.00175

DOI: 10.1016/j.cobeha.2018.12.010

Gijo, M, S. (2023). Symbolic Artificial Intelligence. Innovature https://essymbolic AI.si/symbolic-ai-with-knowledge-graphs-and-ontologies/ [Accessed 13 June 2023]

Gill, S, N. (2023). Artificial Neural Network Applications and Algorithms. https://www.xenonstack.com/blog/artificial-neural-network-applications [Accessed 11 August 2023]

DOI: 10.1109/RBME.2022.3185953

DOI: 10.1007/s11030-021-10217-3

DOI: 10.7150/ijbs.58855

DOI: 10.1109/ACCESS.2017.2788044

DOI: 10.1016/j.ymben.2020.10.005

DOI: 10.1038/nature14539

DOI: 10.1007/s10462-018-9641-3

Peters, M.D.J., Godfrey, C.M., McInerney, P., Soares, C.B., Khalil, H., Parker, D.: The Joanna Briggs Institute Reviewers’ Manual 2015: Methodology for JBI Scoping Reviews. Joanne Briggs Inst. 1–24 (2015)

DOI: 10.1007/978-3-319-65981-7_12

DOI: 10.1016/j.neucom.2022.09.129

Singh, R. (2019). The Rise and Fall of Symbolic AI. Towards Data Science. https://towardsdatascience.com/rise-and-fall-of-symbolic-ai-6b7abd2420f2 [Accessed on 17 August 2023]

DOI: 10.1109/TEVC.2019.2890858

Thomas, W. (2022). What is Hybrid AI? Everything You Need to Know. Fastdatascience https://fastdatascience.com/what-is-hybrid-ai-everything-you-need-to-know/ [Accessed 24 May 2023]

DOI: 10.7326/M18-0850

Verani, E. (2020). Symbolic AI vs Machine Learning in Natural Language Processing. https://www.inbenta.com/symbolic-ai-vs-machine-learning-in-natural-language-processing/ [Accessed 19 June 2023]

DOI: 10.1007/s00330-021-07715-1

DOI: 10.1016/j.neucom.2020.01.085

Younis, M, T. (2022). Rule-Based Expert Systems. ScienceDirect. https://uomustansiriyah.edu.iq/media/lectures/6/6_2022_04_02!07_55_32_PM.pdf [Accessed 17 June 2023]