Introduction
COVID-19 disease, caused by the SARS-CoV-2 virus, was identified in December 2019 in China and declared a global pandemic by the WHO on 11 March 2020. Artificial Intelligence (AI) is a potentially powerful tool in the fight against the COVID-19 pandemic. AI can, for present purposes, be defined as Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision applications to teach computers to use big data-based models for pattern recognition, explanation, and prediction.
Actual and Potential Contributions of AI against COVID-19
There are six areas where AI can contribute to the fight against COVID-19: i) early warnings and alerts, ii) tracking and prediction, iii) data dashboards, iv) diagnosis and prognosis, v) treatments, and cures, and vi) social control.
Treatments and Cures
Even long before the COVID-19 outbreak, AI was lauded for its potential to contribute to new drug discovery. In the case of COVID-19, a number of research labs and data centres have indicated that they are recruiting AI to search for treatments for and a vaccine against COVID-19. The hope is that AI can accelerate both the processes of discovering new drugs as well as for re-purposing existing drugs.
Applications
Chinese firm Baidu is one of the producers of such infrared cameras that uses computer vision to scan crowds. It is reported that these cameras can scan 200 persons per minute and will recognize those whose body temperature exceeds 37.3 degrees. Thermal imaging has however been criticized as being inadequate to identify from a distance a fever in people who are wearing glasses (because scanning the inner tear duct gives the most reliable indication) and because it cannot identify whether a person’s temperature is raised because of COVID-19, or some other reason.
Conclusions
AI is not yet playing a significant role in the fight against COVID-19, at least from the epidemiological, diagnostic and pharmaceutical points of view. Its use is constrained by a lack of data and by too much noisy and outlier data. The creation of unbiased time series data for AI training is necessary. A growing number of international initiatives in this regard is encouraging; however, there is an imperative for more diagnostic testing. Not only for providing training data to get AI models operational, but moreover for more effectively managing the pandemic and reducing its cost in terms of human lives and economic damage.
At the time of writing, the significant efforts of all affected countries have been to shut down their economies through lockdowns, enforcing social distancing, and cancelling events. These measures seem, for now, to have succeeded in slowing down the spread. However, whether these measures are sustainable for more than a couple of weeks is doubtful. According to the Imperial College COVID-19 Response Team, “The major challenge of suppression is that this type of intensive intervention … will need to be maintained until a vaccine becomes available, given that we predict that transmission will quickly rebound if interventions are relaxed.”
Finally, data is central to whether AI will be an effective tool against future epidemics and pandemics. The fear is, as I already mentioned, that public health concerns would trump data privacy concerns. Governments may want to continue the extraordinary surveillance of their citizens long after the pandemic is over. Thus, concerns about the erosion of data privacy are justified.
