The DOI matches its Crossref record; 106 of the 106 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., et al. (2020) · JAMA · doi:10.1001/jama.2020.1585 · Cited by 20,148
Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., & Xia, L. (2020) · Radiology · doi:10.1148/radiol.2020200642 · Cited by 5,685
Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis
Gao, W., Emaminejad, S., Nyein, H. Y. Y., Challa, S., Chen, K., Peck, A., Fahad, H. M., Ota, H., Shiraki, H., Kiriya, D., et al. (2016) · Nature · doi:10.1038/nature16521 · Cited by 5,087
Self-citationMental health and the COVID-19 pandemic
Pfefferbaum, B., & North, C. S. (2020) · New England Journal of Medicine · doi:10.1056/nejmp2008017 · Cited by 4,378
Wearable biosensors for healthcare monitoring
Kim, J., Campbell, A. S., de Ávila, B. E.-F., & Wang, J. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0045-y · Cited by 3,373
To, K. K.-W., Tsang, O. T.-Y., Leung, W.-S., Tam, A. R., Wu, T.-C., Lung, D. C., Yip, C. C.-Y., Cai, J.-P., Chan, J. M.-C., Chik, T. S.-H., et al. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30196-1 · Cited by 3,066
Virtually perfect? Telemedicine for COVID-19
Hollander, J. E., & Carr, B. G. (2020) · New England Journal of Medicine · doi:10.1056/nejmp2003539 · Cited by 2,970
An inflammatory cytokine signature predicts COVID-19 severity and survival
Del Valle, D. M., Kim-Schulze, S., Huang, H.-H., Beckmann, N. D., Nirenberg, S., Wang, B., Lavin, Y., Swartz, T. H., Madduri, D., Stock, A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1051-9 · Cited by 2,605
Cited by
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
Song, Y., Mukasa, D., Zhang, H., & Gao, W. (2021) · Accounts of Materials Research · doi:10.1021/accountsmr.1c00002 · Cited by 237
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
Lan, X., Yu, H., & Cui, L. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.908756 · Cited by 28
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Emerging Telemedicine Tools for Remote COVID-19 Diagnosis, Monitoring, and Management. https://app.cochat.ai/papers/10.1021/acsnano.0c08494
- The paper
- Lukas, H., Xu, C., Yu, Y., & Gao, W. (2020). Emerging Telemedicine Tools for Remote COVID-19 Diagnosis, Monitoring, and Management. ACS Nano. https://doi.org/10.1021/acsnano.0c08494
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.
