Artificial intelligence can help fight the coronavirus through applications such as population screening, notifications of when to seek medical attention, and tracking how the infection is spreading. The COVID-19 pandemic has required intensive work on such applications, but it will unfortunately take time for results to appear. Digital precaution against coronavirus In the face of the coronavirus, digital technologies are vital for both social health and economic performance. A digital response to the COVID-19 pandemic can be delivered in multiple ways. At such a time of rapid development, new applications of artificial intelligence (AI) and machine learning (ML) to screen the population and assess infection risks. Many tests are done to determine who is potentially sick. In China, where the disease was first detected, infrared imaging scanners and handheld thermometers were introduced in many public places, in particular. Chinese artificial intelligence (AI) champion companies have now introduced more advanced artificial intelligence (AI) supported temperature scanning systems, including subway and train stations. The advantage of these systems is that they can remotely scan people and test hundreds of people in a matter of minutes. In China and elsewhere, new AI-powered smartphone apps are being developed to monitor an individual's health and track the geographic spread of the virus. Such applications aim to predict which populations and communities are most susceptible to the adverse effects of a coronavirus outbreak, to enable patients to receive real-time waiting time information from their medical providers, to provide advice and updates to people about their medical conditions. They need to personally visit a hospital and inform people in real time about potential points of infection so that these areas can be avoided. Data access in the face of coronavirus These technologies often need to access data transmitted by mobile phones, including location data. When developing tools, it is also important to develop a framework so that they can be as effective in practice as possible. This requires close coordination between authorities, telecom operators, the hi-tech industry and research institutions. High-tech firms and leading universities, telecom companies may have access to individuals' data, and authorities must ensure that data sharing complies with privacy rules and does not pose a risk of individuals' data being misused. For example, in Belgium, datasets from telecom operators are combined with health data under the supervision of the Belgian Data Protection Authority to create aggregated and anonymized regional-level datasets that can be used to assess how the virus is spreading and which areas are high. Similar initiatives continue in other countries. The real purpose of these efforts is that digital technologies offer real-time monitoring and enable authorities to be more proactive. In Austria, the largest telecom operator has reached an agreement with authorities to provide anonymized data, while a similar anonymized customer data sharing mechanism has been implemented to monitor and analyze population movements in Italy's hard-hit Lombardy region. Apps that protect privacy Academic research can also be helpful in showing how information sharing can be designed while avoiding privacy risks. The Human Dynamics Group at MIT Media Lab, for example, has worked extensively with smartphone data to analyze the behavior of individuals while respecting high privacy standards. MIT's privacy-friendly data mechanisms can be a great basis for designing a data sharing model to limit the spread of COVID-19. Engineers, data scientists, cybersecurity professionals, professors, and researchers from around the world can help prevent the virus from spreading without creating a surveillance situation. They are working on an open source smartphone application. When using encryption methods and no sharing of raw data (personal data does not leave the device), the application checks for overlapping of users' GPS tracks with those of all infected patients (anonymized data provided by health authorities). . This system provides early warnings and personalized information that allows enrollees to understand their own exposure and risks based on earlier contact with infected patients. Such services can only be effective if large numbers of patients and other individuals subscribe. With such information as an input, research on (social) networks attempts to predict how and to what extent the virus will spread, given a predetermined set of parameters and characteristics. Authorities can use these scenarios to prepare contingency plans in a timely manner. Using information about the time individuals spend in a given place and the number of infections that occur there, scientists create spatial models that depict the evolution of contacts between infected people to capture how transmission develops. One of the preliminary findings of such efforts is that the transmission of COVID-19 is more difficult to predict than previous viruses because individuals can carry the virus without showing symptoms and therefore their infection is difficult to detect. A large number of infections in Wuhan appear to be transmitted through such asymptomatic carriers (the Stanford Lin Laboratory estimates that 50% of infected individuals are asymptomatic). Therefore, intensive COVID-19 testing programs (such as those implemented in South Korea) can help by providing data for better performance of these models. AI(artificial intelligence) can also be applied to automatic detection and removal of virus-related misinformation posted on social networks; produce highly accurate and timely CT scans for the detection of viral pneumonia; 3D printing to produce essential tools for intensive health; optimization of clinical trials of drugs and potential vaccines; development of robotic systems to disinfect infected areas; and online systems for medical examination of individuals. Timing is critical (A study on the 1918 influenza pandemic shows that U.S. cities that took non-drug measures at an early stage had a 50% lower death rate. Governments have been criticized for not understanding the severity of the coronavirus situation and not taking coordinated action over time. While the AI community is working hard to deliver applications that can help stop the virus, AI systems are still in their infancy and it will take time for the results of such AI measures to appear. We are still a long way from the end of this tragic story.