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Review
. 2022 Jul:129:102312.
doi: 10.1016/j.artmed.2022.102312. Epub 2022 Apr 30.

A rapid review of machine learning approaches for telemedicine in the scope of COVID-19

Luana Carine Schünke  1 , Blanda Mello  2 , Cristiano André da Costa  3 , Rodolfo Stoffel Antunes  4 , Sandro José Rigo  5 , Gabriel de Oliveira Ramos  6 , Rodrigo da Rosa Righi  7 , Juliana Nichterwitz Scherer  8 , Bruna Donida  9
Affiliations
Review

A rapid review of machine learning approaches for telemedicine in the scope of COVID-19

Luana Carine Schünke et al. Artif Intell Med. 2022 Jul.

Abstract

The COVID-19 pandemic has rapidly spread around the world. The rapid transmission of the virus is a threat that hinders the ability to contain the disease propagation. The pandemic forced widespread conversion of in-person to virtual care delivery through telemedicine. Given this gap, this article aims at providing a literature review of machine learning-based telemedicine applications to mitigate COVID-19. A rapid review of the literature was conducted in six electronic databases published from 2015 through 2020. The process of data extraction was documented using a PRISMA flowchart for inclusion and exclusion of studies. As a result, the literature search identified 1.733 articles, from which 16 articles were included in the review. We developed an updated taxonomy and identified challenges, open questions, and current data types. Our taxonomy and discussion contribute with a significant degree of coverage from subjects related to the use of machine learning to improve telemedicine in response to the COVID-19 pandemic. The evidence identified by this rapid review suggests that machine learning, in combination with telemedicine, can provide a strategy to control outbreaks by providing smart triage of patients and remote monitoring. Also, the use of telemedicine during future outbreaks could be further explored and refined.

Keywords: COVID-19; Machine learning; Survey; Telemedicine.

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Conflict of interest statement

We wish to confirm that there are no known conflicts of interest associated with the article “A Rapid Review of Machine Learning Approaches for Telemedicine in the Scope of COVID-19” sent to possible publication in the Artificial Intelligence in Medicine and there has been no significant financial support for this work that could have influenced its outcome.

We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property.

Figures

Fig. 1
Fig. 1
Search string used for database queries.
Fig. 2
Fig. 2
Flow diagram showing the database search and article selection process using PRISMA guidelines.
Fig. 3
Fig. 3
Quality assessment of the articles.
Fig. 4
Fig. 4
Proposed taxonomy.
See this image and copyright information in PMC

References

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