ReviewArtificial intelligence in medicine2022
A rapid review of machine learning approaches for telemedicine in the scope of COVID-19.
Review in Artificial intelligence in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 35 citations in OpenAlex.
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Artificial Intelligence and Postoperative Monitoring in Plastic Surgery.Plastic surgery (Oakville, Ont.) · 2025Review
- Bridging Distances and Enhancing Care: A Comprehensive Review of Telemedicine in Surgery.Cureus · 2024Review
- The COVID-19 Pandemic Heightens Interest in Cytokine Storm Disease and Advances in Machine Learning Diagnosis, Telemedicine, and Primordial Prevention of Rheumatic Diseases.European journal of rheumatology · 2024Review
- FaceMotionPreserve: a generative approach for facial de-identification and medical information preservation.Scientific reports · 2024Article
- CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection.Diagnostics (Basel, Switzerland) · 2024Article
- Greenway of Digital Health Technology During COVID-19 Crisis: Bibliometric Analysis, Challenges, and Future Perspective.Advances in experimental medicine and biology · 2024Review
- Disrupting the infectious disease ecosystem in the digital precision health era innovations and converging emerging technologies.Antimicrobial agents and chemotherapy · 2023Article
- Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation.Frontiers in digital health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.