An Artificial Intelligence (AI) platform named BlueDot detected clusters of an unusual respiratory illness in Wuhan before the Chinese Authorities knew of the disease. Nineteen days later, the World Health Organization and Centers for Disease Control and Prevention alerted public health officials of the infectious disease. Since then, various AI programs have joined in tracking the spread of the disease, testing people, as well as finding a cure and vaccine.
The working of the AI algorithms in tackling coronavirus
The BlueDot AI platform relies on enormous amounts of data, natural language processing, and machine learning in detecting the outbreak. The AI program pulls data from various sources such as official public health data, airline ticketing, digital media, livestock health reports, and population data. The program looks for a cluster of symptoms that might indicate a concentration of the disease within an area. The BlueDot program can analyze data in over 65 languages. Airline ticketing information helps predict where the infected people might be travelling to. In the case of the spread of the coronavirus, BlueDot was able to identify that most of the infected people were travelling to Bangkok, Hong Kong, Tokyo, Taipei, Phuket, Seoul, and Singapore. These cities are among the list of places to have the highest number of coronavirus cases outside China.
Apart from the prediction of the coronavirus outbreak, the BlueDot program also successfully predicted other disease outbreaks such as the Zika outbreak in Florida in 2016 and the Ebola outbreak in West Africa in 2014. Kamran Khan, founder and CEO of BlueDot and professor of medicine and public health at the University of Toronto, says that the program was inspired by the SARS outbreak when he worked as an epidemiologist in Toronto.
Possible shortcomings of the AI program
Although the program has been successful, the technology needs human interventions to make a conclusive decision. There is also the possibility of the program detecting false positives, which would lead to hysteria if not adequately investigated. Additionally, the problem of inaccurate data and inadequate training could undermine the effectiveness of the program. Despite these shortcomings, the program plays a crucial role in complementing human efforts in analyzing the massive amounts of data. The AI program also assists in focusing efforts on areas that are at a higher risk of having an outbreak.
Other players capitalizing on AI to combat coronavirus
Other tech companies are utilizing the advantages of big data to help in combating the spread of coronavirus. Researchers have created various AI algorithms that can help track the spread of infectious diseases in real-time. AI algorithms are helping understand how various preventive measures can affect the spread of the virus. AI is also playing a pivotal role in the creation of the treatment of coronavirus. Other companies utilizing big data in combating the spread of coronavirus include Metabiota and Nanox. Nanox has created a digital X-ray system that utilizes AI cloud-based software to diagnose new disease infections. The Israeli-based firm can produce an early diagnosis by utilizing massive image database of infections, radiologist matching, professional diagnostic reviews, and annotations, as well as assistive AI technology. The company has developed standalone X-ray machines in airports that can help test thousands of people in a day. The company has already received $26 million strategic investment from Foxconn as well as thousands of orders of its equipment from countries such as Australia, New Zealand, and Norway.
Possible ethical conflicts in the use of AI against coronavirus
Like other technologies, the use of AI will raise various ethical issues. The possibility of the technology resulting in practices such as digital redlining is raising ethical concerns. This situation could arise if the AI systems fail to include data from marginalized areas heavily affected by the virus, hence denying the people access to health intervention. Such areas have low infiltration of digital technology, thus preventing their data from appearing in analyses. Issues such as privacy and access to social media data could also raise concerns over possible misuse of the data. Additionally, the lack of an explicit mechanism of obtaining permission from the users, as well as the lack of clarity over the type of data being collected, could raise ethical concerns. Health concerns due to the exposure to technologies such as x-rays are also of ethical considerations.