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Emerging Telemedicine Tools for Remote COVID-19 Diagnosis, Monitoring, and Management
Abstract
The management of the COVID-19 pandemic has relied on cautious contact tracing, quarantine, and sterilization protocols while we await a vaccine to be made widely available. Telemedicine or mobile health (mHealth) is well-positioned during this time to reduce potential disease spread and prevent overloading of the healthcare system through at-home COVID-19 screening, diagnosis, and monitoring. With the rise of mass-fabricated electronics for wearable and portable sensors, emerging telemedicine tools have been developed to address shortcomings in COVID-19 diagnostics, monitoring, and management. In this Perspective, we summarize current implementations of mHealth sensors for COVID-19, highlight recent technological advances, and provide an overview on how these tools may be utilized to better control the COVID-19 pandemic.
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Bo, Z., She, J., Wang, Y., & Ma, X. (2020) · doi:10.21203/rs.3.rs-18079/v1 · Cited by 79
Alsaeedy, A. A. R., & Chong, E. K. P. (2020) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2020.3002447 · Cited by 78
Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19
Zhu, G., Li, J., Meng, Z., Yu, Y., Li, Y., Tang, X., Dong, Y., Sun, G., Zhou, R., Wang, H., et al. (2020) · Discrete Dynamics in Nature and Society · doi:10.1155/2020/6152041 · Cited by 77
Self-citationPotential biochemical markers to identify severe cases among COVID-19 patientsPreprint
Xiang, J., Wen, J., Yuan, X., Xiong, S., Zhou, X. Q., Liu, C., & Min, X. (2020) · medRxiv · doi:10.1101/2020.03.19.20034447 · Cited by 74
Dorneles, G. P., Passos, A. A. Z. D., Romão, P. R. T., & Peres, A. (2020) · Current Pharmaceutical Design · doi:10.2174/1381612826666200305125210 · Cited by 62
Reducing False Negatives in COVID-19 Testing by Using Microneedle-Based Oropharyngeal Swabs
Chen, W., Cai, B., Geng, Z., Chen, F., Wang, Z., Wang, L., & Chen, X. S. (2020) · Matter · doi:10.1016/j.matt.2020.09.021 · Cited by 52
MEMS Biosensors and COVID-19: Missed Opportunity
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Zhang, H., Dimitrov, D. S., Simpson, L. A., Singh, B., Plaks, N., Penney, S., Charles, J., Sheehan, R. R., Flammini, S., Murphy, S. N., et al. (2020) · JMIR Formative Research · doi:10.2196/19533 · Cited by 42
Miller, E. A., Baniya, S., Osorio, D. A., Maalouf, Y. J. A., & Sikes, H. D. (2017) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2017.11.050 · Cited by 41
The Impact of COVID-19 on CGM Use in the Hospital
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Hoste, A. C. R., Venteo, Á., Fresco‐Taboada, A., Tapia, I., Monedero, A., López, L., Jebbink, M. F., Pérez‐Ramírez, E., Jiménez‐Clavero, M. Á., Almonacid, M., et al. (2020) · Diagnostic Microbiology and Infectious Disease · doi:10.1016/j.diagmicrobio.2020.115167 · Cited by 29
Acetylated K676 TGFBIp as a severity diagnostic blood biomarker for SARS-CoV-2 pneumonia
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A Review of the Role of Artificial Intelligence in Healthcare
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915
Ambulatory Cardiovascular Monitoring Via a Machine‐Learning‐Assisted Textile Triboelectric Sensor
Fang, Y., Zou, Y., Xu, J., Chen, G., Zhou, Y., Deng, W., Zhao, X., Roustaei, M., Hsiai, T. K., & Chen, J. (2021) · Advanced Materials · doi:10.1002/adma.202104178 · Cited by 380
Self-Powered Wearable Biosensors
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Self-citationPeyroteo, M., Ferreira, I. A., Elvas, L. B., Ferreira, J. C., & Lapão, L. V. (2021) · JMIR mHealth and uHealth · doi:10.2196/28285 · Cited by 190
Application of telemedicine in COVID-19: a bibliometric analysis
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Metadata
- Authors
- Lukas, H., Xu, C., Yu, Y., & Gao, W.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-12-14
- Volume
- 14
- Issue
- 12
- Pages
- 16180-16193
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W3112693771
- Cited by
- 265
- References
- 106
- FWCI
- 5.58
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.