| Literature DB >> 34295908 |
Yu-Ting Shen1, Liang Chen2, Wen-Wen Yue1, Hui-Xiong Xu1.
Abstract
In the year 2020, the coronavirus disease 2019 (COVID-19) crisis intersected with the development and maturation of several digital technologies including the internet of things (IoT) with next-generation 5G networks, artificial intelligence (AI) that uses deep learning, big data analytics, and blockchain and robotic technology, which has resulted in an unprecedented opportunity for the progress of telemedicine. Digital technology-based telemedicine platform has currently been established in many countries, incorporated into clinical workflow with four modes, including "many to one" mode, "one to many" mode, "consultation" mode, and "practical operation" mode, and has shown to be feasible, effective, and efficient in sharing epidemiological data, enabling direct interactions among healthcare providers or patients across distance, minimizing the risk of disease infection, improving the quality of patient care, and preserving healthcare resources. In this state-of-the-art review, we gain insight into the potential benefits of demonstrating telemedicine in the context of a huge health crisis by summarizing the literature related to the use of digital technologies in telemedicine applications. We also outline several new strategies for supporting the use of telemedicine at scale.Entities:
Keywords: COVID-19; SARS-CoV-2; infectious diseases; respiratory diseases; telemedicine
Year: 2021 PMID: 34295908 PMCID: PMC8289897 DOI: 10.3389/fmed.2021.646506
Source DB: PubMed Journal: Front Med (Lausanne) ISSN: 2296-858X
Figure 1Telemedicine application model in context of the coronavirus disease 2019 outbreak.
Figure 2The nine stages of transformational change of telemedicine (19).
Figure 3Telemedicine based on artificial intelligence and big data technologies for the surveillance of COVID-19 pandemic. (A) Big data based modeling study: preparedness and vulnerability of African countries against importations of COVID-19 (35). (B) Online contagious COVID-19 surveillance mapping provided by HealthMap (36). (C) Geographic distribution of population outflow from Wuhan through January 24, 2020 (in red) and the confirmed COVID-19 cases in other Chinese prefectures as of February 19, 2020 (37). (D,E) Predictive model supported by the population outflow data from Wuhan: (D) the surface displays the fitted performance of this epidemiological model, with dots representing actual number of confirmed cases, and (E) the risk scores over time present a dynamic picture of the shifting transmission risks in different prefectures (37). SPAR, State Party Self-Assessment Annual Reporting; IDVI, Infectious Disease Vulnerability Index.
Digital technology based telemedicine and their impact on public-health strategies during the COVID-19 pandemic.
| IoT, Big data | Worldometer | – | – | At home/In clinic | Surveillance the status of pandemic | Presents a real time update on the exact number of people infected with COVID-19 worldwide, including the daily new cases and severity of disease, as well as disease distribution by countries |
| IoT, Big data | Johns Hopkins University | The United States | Develop a real time tracking map for following the COVID-19 patients | At home/In clinic | Surveillance the status of pandemic | Following the COVID-19 patients around the world |
| Big data | Wu et al. ( | China | Conduct a modeled study on “nowcasting” based on global databases | At home/In clinic | Surveillance the status of pandemic | Forecast the COVID-19 disease activity inside and outside China, which could also be used by authorities for public health control worldwide |
| Big data | Gilbert et al. ( | Belgium; France; the United States; Côte d'Ivoire; United Kingdom | Evaluate the vulnerability and preparedness of African countries in coping with COVID-19 | At home/In clinic | Surveillance the status of pandemic | Countries with the highest importation risk (i.e., Egypt, Algeria, and South Africa) have moderate to high capacity to respond to outbreaks. Countries at moderate risk (i.e., Nigeria, Ethiopia, Sudan, Angola, Tanzania, Ghana, and Kenya) have variable capacity and high vulnerability. Three clusters of countries that share the same exposure to the risk originating from the provinces of Guangdong, Fujian, and the city of Beijing, respectively. |
| Big data | HealthMap ( | The United States | Develop an online surveillance-mapping tool | At home/In clinic | Surveillance the status of pandemic | Surveilling COVID-19 pandemic |
| Big data | SORMAS ( | Germany | Develop an online surveillance-mapping tool | At home/In clinic | Surveillance of the COVID-19 pandemic | Surveilling COVID-20 pandemic |
| Big data | Qin et al. ( | China | Exploite the big data technique | At home/In clinic | Surveillance the COVID-19 pandemic | By employing techniques such as subset selection method, new COVID-19 suspected and confirmed cases could be detected 6-9 and 10 days in advance, respectively. |
| Big data, AI | Yang et al. ( | China | Establish a modified SEIR model | At home/In clinic | Surveillance the COVID-20 pandemic | A 5-day delay in the adoption of stringent public health measures by the Chinese authorities would have leaded to a COVID-19 epidemic size increased by up to three times. Also, loosening or lifting the lock-down intervention in the Hubei province would cause a second peak by mid-March until late April. |
| Big data, AI | Blue Dot | Canada | Develop an AI-based surveillance system | At home/In clinic | Surveillance the COVID-19 pandemic | Reveal news of the pandemic, which is widely regarded as the first organization to detect the epidemic outbreak in late December, 2019, well ahead of any other international institution and agency. |
| Big data, AI | Zhang et al. ( | – | Develop an AI-based surveillance and prediction system | At home/In clinic | Surveillance the COVID-19 pandemic | A real-time system to surveilling and sentiment prediction COVID-19 pandemic. |
| AI | Zivkovic et al. ( | – | Improve the current time-series prediction algorithms system | At home/In clinic | Surveillance the COVID-19 pandemic | Achieved good predictive efficacy of COVID-19 pandemic by using a hybrid algorithmic approach that combines machine learning, an enhanced beetle tentacle search population intelligent metaheuristic algorithm. |
| AI | Yan et al. ( | China | Develop an “online self-assessment tool” supported by AI | At home | Screening and Triage COVID-19 patients | Help individuals self-evaluate the risk of COVID-19. |
| AI | Srinivasa Rao et al. ( | the United States | Provided a framework based on AI algorithm that could enable quick identification of COVID-19 cases | At home | Screening and Triage COVID-19 patients | Depending on these replies, this algorithm could send alerts to the respondent, as well as clinics or the mobile health units, for further health visits and case confirmation. |
| AI | D'Angelo et al. ( | Italy | Establish a convolutional deep neural network-based human activity classifier | At home/In clinic | Screening and Triage COVID-19 patients | Improve the performance of the new COVID-19 patient tracking application by using a human activity classifier based on convolutional neural networks and multichannel images. |
| Robot | Global Health Security ( | China | Develop robots to treat and test Covid-19 patients | At home/In clinic | Screening and Triage COVID-19 patients | Develop robots to treat and test Covid-19 patients in a bid to protect health workers. |
| Blockchain | Ting et al. ( | Singapore | Hospitals collaborating with blockchain pharmacies and companies | In clinic | Treatment of COVID-19 patients | Deliver the medication of patients to their doorsteps, thus allowing hospitals deliver medications timely also with accurate tracking. |
| 5G, Blockchain | Hong et al. ( | China | Conduct CT scanning | In clinic | Diagnosis of COVID-19 patients | The first reported case of remote CT scanning during the COVID-19 pandemic. |
| AI, Big data | Zhang et al. ( | China | Reported an AI-powered CT diagnostic system | In clinic | Diagnosis of COVID-19 patients | Diagnose COVID-19 with an accuracy of 92.49%, and had been made available globally to assist the clinicians to combat COVID-19. |
| 5G, Blockchain | Li et al. ( | China | 5G-based teleultrasound network | In clinic | Diagnosis and treatment of COVID-19 patients | Facilitate “on-line” imaging data transmission, and the further “real-time” diagnosis or operation guidance for COVID-19 patients, especially those in ICUs. |
| 5G, Robot | Li et al. ( | China | 5G remote robotic ultrasound diagnostic” system | In clinic | Diagnosis of COVID-19 patients | Enable real-time remote control for ultrasound scanning, thus eliminating exposure to COVID-19 to the greatest extent. |
| AI | Li et al. ( | China | AI diagnostic model based on chest CT | In clinic | Diagnosis of COVID-19 patients | Distinguishes COVID-19 from community acquired pneumonia with the sensitivity and specificity of 90% and 96%, with an AUC of 0.96 |
| AI, Big data | CLEW ( | Israeli | AI-powered tele-ICU system | In clinic | Monitoring status of COVID-19 patients | Support monitoring COVID-19 patients status with certain respiratory deterioration prediction models, which was later installed in two Israeli hospitals. |
| 5G, Robot | Tian et al. ( | China | 5G based tele-robotic spinal surgery | In clinic | Mitigation of the impact to healthcare system indirectly related to COVID-19 | All the 12 patients treated with this technology had substantial relief from their symptoms, while without any intraoperative adverse event. |
| AI | Wang ( | China | Tree Holes Rescue (a kind of AI psychological programme) | At home | Mitigation of the impact to healthcare system indirectly related to COVID-20 | Recognized individuals at risk of suicide by monitoring and analyzing the messages posted on Weibo, and further alerting the designated volunteers to take action accordingly. |
5G, fifth generation; AI, artificial intelligence; IoT, internet of things; CT, computed tomography; US, ultrasound; COVID-19, coronavirus disease 2019; WHO, World Health Organization; ICU, intensive care unit; SEIR, Susceptible-Exposed-Infectious-Removed.
Figure 4Telemedicine system for the screening and triage COVID-19 patients. (A–C) Conceptual framework of collecting data and identifying possible COVID-19 cases (52); a geographic region (e.g., a village, town, county, or city) with households in it (A). The respondents and non-respondents of a phone-based web survey (B). The possible identified COVID-19 cases among the respondents and non-respondents of the survey (78) (C). (D,E) Fangcang shelter hospitals equipped with telemedicine system and their key characteristics and essential functions (79).
Figure 5Telemedicine based on fifth-generation (5G) network and robotic technology for disease diagnosis and treatment during the COVID-19 outbreak. (A) 5G telemedicine platform of Sichuan Province of China developed during this pandemic. (B) The web-based real-time video tele-consultation provided by a multidisciplinary medical team based on “5G Dual Gigabit network” to deal with cases vulnerable to severe COVID-19 in western China (57). (C,D) 5G remote robotic ultrasound diagnostic system used in Fangcang shelter hospitals (82). (E) 5G network-based tele-ultrasound system tele-robotic spinal surgery including screw planning at master control room and (F) K-wire placement (63).
Figure 6Digital technology-based tele-radiology system used during this COVID-19 outbreak. (A) The artificial intelligence (AI) framework for COVID-19 diagnosis and prognosis prediction based on CT imaging (58). (B,C) Schematic diagram of the basic structure of tele-ultrasound system based on 5G internet cloud-based data transfer, the “many to one” mode (A) and “one to many” mode (59, 60). COVID-19, coronavirus disease 2019; NCP, novel coronavirus pneumonia; CRP, C-reactive protein; CT, computed tomography; 5G, fifth generation; PC, picture archiving and communication system; IOU, intraoperative ultrasound; ICU, intensive care unit.
Figure 7The proposed data flow of telemedicine for clinical care: to maximize clinical care though telemedicine system, a closed loop is quite necessary, which involves healthcare data derived from patients and practitioners; transferred via 5G-Cloud internet; interpreted by the patients, medical practitioners, or with certain automated platforms (e.g., AI, robot, and big data analysis); and returned back to the patients and medical staff for better clinical decisions. And in turn, the large-scale shared cloud-based data would provide a great opportunity for AI development. 5G, fifth generation; AI, artificial intelligence; CT, computed tomography; US, ultrasound.
Figure 8Summative scheme of digital technology-based telemedicine used during the COVID-19 outbreak with its advantages, challenges, and strategies for future wide application. COVID-19, coronavirus disease 2019; 5G, fifth generation; IoT, internet of things; AI, artificial intelligence.