The CLAIRE COVID-19 initiative: approach, experiences and recommendations

Abstract

A volunteer effort by Artificial Intelligence (AI) researchers has shown it can deliver significant research outcomes rapidly to help tackle COVID-19. Within two months, CLAIRE's self-organising volunteers delivered the World's first comprehensive curated repository of COVID-19-related datasets useful for drug-repurposing, drafted review papers on the role CT/X-ray scan analysis and robotics could play, and progressed research in other areas. Given the pace required and nature of voluntary efforts, the teams faced a number of challenges. These offer insights in how better to prepare for future volunteer scientific efforts and large scale, data-dependent AI collaborations in general. We offer seven recommendations on how to best leverage such efforts and collaborations in the context of managing future crises.

Emerging technologies for the management of COVID19: A review

“…Autonomous robots, such as the floor-cleaning bots that are already accessible as consumer products, can do more than just clean in hospitals [19] . Similar to “drone technology,” other autonomous technologies such as “robots and autonomous vehicles (AVs)” have made important advancements in thebattle against the pandemic [20] .In some countries robots are used for taking care of patients as well as for cleaning and sanitising purpose, in case of severly infected patient [21] …”

Questioning causality on sex, gender and COVID-19, and identifying bias in large-scale data-driven analyses: the Bias Priority Recommendations and Bias Catalog for Pandemics

“…In COVID-19-like pandemic scenarios we recommend following the CLAIRE Initiative COVID-19 recommendations [9] to facilitate data-driven AI solutions.…”

“…Before performing a clinical research study, this framework must be shared, understood and agreed upon among clinicians, physicians, epidemiologists, biostatisticians and data scientists. As recommended by [9], it should including open data space, infrastructure, open licenses, data management plan, and data standardization scheme.…”