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Cited by in F6Publishing

For:	Lee Y, Kim YS, Lee DI, Jeong S, Kang GH, Jang YS, Kim W, Choi HY, Kim JG, Choi SH. The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection. Sci Rep 2022;12:1234. [PMID: 35075153 DOI: 10.1038/s41598-022-05069-2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]

For:

Lee Y, Kim YS, Lee DI, Jeong S, Kang GH, Jang YS, Kim W, Choi HY, Kim JG, Choi SH. The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection. Sci Rep 2022;12:1234. [PMID: 35075153 DOI: 10.1038/s41598-022-05069-2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]

Number	Citing Articles
1	Lee Y, Kim YS, Lee DI, Jeong S, Kang GH, Jang YS, Kim W, Choi HY, Kim JG. Comparison of the Diagnostic Performance of Deep Learning Algorithms for Reducing the Time Required for COVID-19 RT-PCR Testing. Viruses 2023;15. [PMID: 36851519 DOI: 10.3390/v15020304] [Reference Citation Analysis]
2	Wang L, Li Z. Smart Nanostructured Materials for SARS-CoV-2 and Variants Prevention, Biosensing and Vaccination. Biosensors (Basel) 2022;12. [PMID: 36551096 DOI: 10.3390/bios12121129] [Reference Citation Analysis]
3	Wang J. A Review of Deep Learning-Based Methods for the Diagnosis and Prediction of COVID-19. International Journal of Patient-Centered Healthcare 2022;12:1-17. [DOI: 10.4018/ijpch.311444] [Reference Citation Analysis]
4	Özbilge E, Sanlidag T, Ozbilge E, Baddal B. Artificial Intelligence-Assisted RT-PCR Detection Model for Rapid and Reliable Diagnosis of COVID-19. Applied Sciences 2022;12:9908. [DOI: 10.3390/app12199908] [Reference Citation Analysis]
5	Adler-Milstein J, Aggarwal N, Ahmed M, Castner J, Evans BJ, Gonzalez AA, James CA, Lin S, Mandl KD, Matheny ME, Sendak MP, Shachar C, Williams A. Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis. NAM Perspect 2022;2022. [PMID: 36713769 DOI: 10.31478/202209c] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
6	Peng L, Wang C, Tian G, Liu G, Li G, Lu Y, Yang J, Chen M, Li Z. Analysis of CT scan images for COVID-19 pneumonia based on a deep ensemble framework with DenseNet, Swin transformer, and RegNet. Front Microbiol 2022;13:995323. [DOI: 10.3389/fmicb.2022.995323] [Reference Citation Analysis]
7	Dall'Alba G, Casa PL, Abreu FP, Notari DL, de Avila E Silva S. A Survey of Biological Data in a Big Data Perspective. Big Data 2022. [PMID: 35394342 DOI: 10.1089/big.2020.0383] [Reference Citation Analysis]