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Review
. 2024 Jan 11:6:1292466.
doi: 10.3389/frai.2023.1292466. eCollection 2023.

A survey on the role of artificial intelligence in managing Long COVID

Ijaz Ahmad  1 , Alessia Amelio  2 , Arcangelo Merla  2 , Francesca Scozzari  3
Affiliations
Review

A survey on the role of artificial intelligence in managing Long COVID

Ijaz Ahmad et al. Front Artif Intell. .

Abstract

In the last years, several techniques of artificial intelligence have been applied to data from COVID-19. In addition to the symptoms related to COVID-19, many individuals with SARS-CoV-2 infection have described various long-lasting symptoms, now termed Long COVID. In this context, artificial intelligence techniques have been utilized to analyze data from Long COVID patients in order to assist doctors and alleviate the considerable strain on care and rehabilitation facilities. In this paper, we explore the impact of the machine learning methodologies that have been applied to analyze the many aspects of Long COVID syndrome, from clinical presentation through diagnosis. We also include the text mining techniques used to extract insights and trends from large amounts of text data related to Long COVID. Finally, we critically compare the various approaches and outline the work that has to be done to create a robust artificial intelligence approach for efficient diagnosis and treatment of Long COVID.

Keywords: Long COVID; PASC; artificial intelligence; deep learning; machine learning; post-acute sequelae of SARS CoV-2 infection.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
Mindmap illustrating the different organ systems on which the Long COVID has effects (Davis et al., 2023).
Figure 2
Figure 2
The literature selection process.
Figure 3
Figure 3
A conceptual framework for the generation and assessment of PASC symptoms using NLP-based text mining methods.
See this image and copyright information in PMC

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