Considerations, Good Practices, Risks and Pitfalls in Developing AI Solutions Against COVID-19
It provides guidelines for developing AI solutions against COVID-19, but is incremental as a follow-up review article.
The paper reviews AI applications for COVID-19 across molecular, clinical, and societal scales, assessing their maturity, feasibility, and operationalization potential, while summarizing risks, pitfalls, and best practices for deployment.
The COVID-19 pandemic has been a major challenge to humanity, with 12.7 million confirmed cases as of July 13th, 2020 [1]. In previous work, we described how Artificial Intelligence can be used to tackle the pandemic with applications at the molecular, clinical, and societal scales [2]. In the present follow-up article, we review these three research directions, and assess the level of maturity and feasibility of the approaches used, as well as their potential for operationalization. We also summarize some commonly encountered risks and practical pitfalls, as well as guidelines and best practices for formulating and deploying AI applications at different scales.