Table 8.
Most global cited documents
| Rank | First author and Journal | Paper | DOI | Total Citations |
|---|---|---|---|---|
| 1 | Wynants L, 2020, Bmj-Brit Med J | Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal | 10.1136/bmj.m1328 | 250 |
| 2 | Peeri NC, 2020, Int J Epidemiol | The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned? | 10.1093/ije/dyaa033 | 231 |
| 3 | Li SJ, 2020, Int J Env Res Pub He | The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users | 10.3390/ijerph17062032 | 168 |
| 4 | Yang ZF, 2020, J Thorac Dis | Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions | 10.21037/jtd.2020.02.64 | 148 |
| 5 | Li L, 2020, Radiology | Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy | 10.1148/radiol.2020200905 | 116 |
| 6 | Li DS, 2020, Korean J Radiol | False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases | 10.3348/kjr.2020.0146 | 115 |
| 7 | Ivanov D, 2020, Transport Res E-Log | Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case | 10.1016/j.tre.2020.101922 | 97 |
| 8 | Ton AT, 2020, Mol Inform | Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds | 10.1002/minf.202000028 | 88 |
| 9 | Ozturk T, 2020, Comput Biol Med | Automated detection of COVID-19 cases using deep neural networks with X-ray images | 10.1016/j.compbiomed.2020.103792 | 71 |
| 10 | Shen B, 2020, Cell | Proteomic and Metabolomic Characterization of COVID-19 Patient Sera | 10.1016/j.cell.2020.05.032 | 70 |
| 11 | Yan L, 2020, Nat Mach Intell | An interpretable mortality prediction model for COVID-19 patients | 10.1038/s42256-020–0180-7 | 66 |
| 12 | Apostolopoulos ID, 2020, Phys Eng Sci Med | Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks | 10.1007/s13246-020–00,865-4 | 65 |
| 13 | Jiang XG, 2020, Cmc-Comput Mater Con | Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity | 10.32604/cmc.2020.010691 | 54 |
| 14 | Vigneswaran Y, 2020, J Gastrointest Surg | What Is the Appropriate Use of Laparoscopy over Open Procedures in the Current COVID-19 Climate? | 10.1007/s11605-020–04,592-9 | 49 |
| 15 | Mccall B, 2020, Lancet Digit Health | COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread | 10.1016/S2589-7500(20)30,054–6 | 49 |
| 16 | Mei XY, 2020, Nat Med | Artificial intelligence–enabled rapid diagnosis of patients with COVID-19 | 10.1038/s41591-020–0931-3 | 44 |
| 17 | Ciotti M, 2020, Crit Rev Cl Lab Sci | The COVID-19 pandemic | 10.1080/10408363.2020.1783198 | 42 |
| 18 | Santosh KC, 2020, J Med Syst-a | AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data | 10.1007/s10916-020–01,562-1 | 42 |
| 19 | Allam Z, 2020, Healthcare-Basel | On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management | 10.3390/healthcare8010046 | 40 |
| 20 | Eccleston C, 2020, Pain | Managing patients with chronic pain during the COVID-19 outbreak: considerations for the rapid introduction of remotely supported (eHealth) pain management services | 10.1097/j.pain.0000000000001885 | 38 |