As novel COVID-19 cases continue to rise at an alarming rate, predictions also paint a gloomy picture of the next months to come. Researchers worldwide are also going the extra mile to understand, mitigate, and restrain this deadly spread. In this blog, we have published about how AI Applications to Combat COVID-19 are there to manage global response; and we have focused on three areas: individual patient diagnosis and treatment, protein and drug discovery, and the social-economic impact of coronavirus.
AI + Medical Research Against COVID-19
When considering medical imaging, an AI model performs specific tasks; for example, analyzing CT lung scans more and even more than a medical professional. So, to combat the current pandemic, using machine learning (ML) approaches for fast-diagnostics can save many lives. In many studies, AI models were adept to detect potential COVID-19 in patients; whereas the rest used a human-in-the-loop approach to the lower required time to mark the disease. In a nutshell, all these efforts are in the initial phase; albeit, the preliminary results are a sigh of relief.
Moreover, there’s continuous clinical research taking place to discover drugs to fight off this fatal disease. Scientists are working to identify present drugs that might be available for the treatment of COVID-19 patients. One of the examples is the publicized case, Chloroquine, and Hydroxychloroquine – these drugs are in use to treat malaria and have shown some promising results. Along with this, there are continuous efforts to find new drugs that can help in combating the pandemic.
Furthermore, AI systems, approaches, and models can act as a consolidated form of information sharing that may turn out to help train other specialists and can be available on a large-scale. To enable the sharing of such information, clinical protocols and data sharing mechanisms should be designed and data governance frameworks must be created.
AI Shedding Some Light on Public Implications of COVID-19
COVID-19 pandemic has put a strain on our society since it has affected people at every level; from businesses shutting down, to economic plunge, schools seizing with online classes, and people are in isolation at home. A fickle has been also seen in the advisory at both national and local levels as new information and model forecasts become more accessible. Above all, AI models should be flexible enough to acclimate the changing protocols and other processes. With a large number of potential factors affecting the dynamics of the disease, AI models can be a crucial source for epidemiologists in approaching the fundamental complex behavior.
Also, the ability to measure the propagation of information related to the pandemic helps curb the spread of erroneous information and inaccuracies, which are dominant. Furthermore, social media platforms have become the main channels for spreading the news about COVID-19. Even though national and international organizations have been using these platforms to communicate with the public, an ‘infodemic’ has been also seen, overwhelming people with heaps of details.
Conclusion
In the end, the success of the global effort to use AI Applications to tackle the COVID-19 pandemic depends on adequate access to data. Machine Learning particularly requires a massive amount of data and computing power to create and train advanced algorithms along with neural network architectures.
Some of the systems swotted in this research are yet to have the operational maturity which is in need to fight the virus at the current stage. Though, they’ve moved the needle in the right direction. Also, to install these efforts, we need to work together to outline a road map for AI applications and comprehend how this technology will help, as the pandemic keeps evolving.
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