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 tax-
onomy 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.
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 tax-
onomy 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.
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 tax-
onomy 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.
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 tax-
onomy 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.
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 tax-
onomy 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.
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 tax-
onomy 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.
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 tax-
onomy 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.
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 tax-
onomy 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.
