Can <scp>AI</scp> help in the fight against <scp>COVID</scp> ‐19?

Abstract

Artificial intelligence is being used in several different ways to curb the current pandemic while demonstrating its potential to be even more effective for the next one The corolnavirus disease 2019 (COVID-19) pandemic has accelerated efforts to incorporate artificial intelligence (AI) into clinical care at a time when, in many countries, health care systems are facing unprecedented strain on their resources. Before COVID-19, AI was already permeating into health care,1 and reviews are emerging of how it may assist in efforts to combat the current pandemic.2-5 We describe several applications of AI relevant to COVID-19, some having had immediate clinical application, others awaiting further refinement and evaluation. The AI-automated HealthMap system at Boston Children's Hospital first alerted the world about the novel coronavirus on 30 December 2019, with a Canadian-based AI model, BlueDot, issuing a similar alert a day later. Researchers warned of the top 20 destination cities for passenger arrivals from Wuhan to which the disease could spread.6 These AI-enabled early warning systems use natural language processing to scan social media, online news articles and government reports for signs of emerging pandemics to help inform governments and agencies such as the World Health Organization. AI-assisted analysis and modelling have also helped reconstruct the progression of an outbreak, elucidate transmission pathways, identify and trace contacts, and determine real or expected impacts of various public health control measures (Box 1).7-11 How data are collected, and how these algorithms are deployed, raise difficult issues of consent, privacy, ethics and trade-offs between public and private good. Some countries, like Taiwan, have mandated a top-down approach to data harvesting. Others, including Australia, encourage individuals to voluntarily download apps to input symptoms and COVID-19 status and permit health authorities to access this information in identifying potential contacts. However, the efficacy of app-mediated contact tracing depends on the level of population uptake, its ability to accurately detect infectious contacts, and the extent of adherence to self-isolation by notified contacts.12 Expert position statements regarding design, scope, security and usage of such apps aim to prevent mission creep towards unauthorised surveillance of society at large.13 Detecting COVID-19 in most health systems currently involves testing symptomatic patients presenting to stand-alone fever clinics, general practices or emergency departments. This takes time, consumes personal protective equipment and testing reagents, and poses transmission risk to staff. Digital symptom checkers soliciting information about symptoms and risk factors may screen out individuals with very low likelihood of COVID-19 who do not require testing. In a pre-clinical study using hypothetical cases, an AI-powered chatbot identified patients with COVID-19 with sensitivity, specificity and overall diagnostic accuracy of 97%, 96% and 96%, respectively.1 However, a side-by-side test of eight different chat boxes on the same set of symptoms produced conflicting results,15 suggesting the need to identify all the data elements necessary for highest accuracy. Data from phone hotlines used to pre-screen individuals based on travel history and symptoms,16 and from sensors (cameras, microphones, temperature and inertial sensors) embedded within smartphones, can all be used to detect COVID-19.17 Neural networks embedded in cameras can distinguish patterns of tachypnoea caused by COVID-19 from those caused by influenza or the common cold.18 AI-powered thermal-scanning face cameras, capable of screening up to 200 people per minute, are being used by some Australian private hospitals to remotely detect people with fevers, sweating and discolouration, and prevent them entering public spaces.19 Diagnosing COVID-19 in sick patients presenting to hospital is currently perfo