The COVID-19 pandemic, caused by the severe acute respiratory syndrome
coronavirus 2, emerged into a world being rapidly transformed by artificial
intelligence (AI) based on big data, computational power and neural networks.
The gaze of these networks has in recent years turned increasingly towards
applications in healthcare. It was perhaps inevitable that COVID-19, a global
disease propagating health and economic devastation, should capture the
attention and resources of the world's computer scientists in academia and
industry. The potential for AI to support the response to the pandemic has been
proposed across a wide range of clinical and societal challenges, including
disease forecasting, surveillance and antiviral drug discovery. This is likely
to continue as the impact of the pandemic unfolds on the world's people,
industries and economy but a surprising observation on the current pandemic has
been the limited impact AI has had to date in the management of COVID-19. This
correspondence focuses on exploring potential reasons behind the lack of
successful adoption of AI models developed for COVID-19 diagnosis and
prognosis, in front-line healthcare services. We highlight the moving clinical
needs that models have had to address at different stages of the epidemic, and
explain the importance of translating models to reflect local healthcare
environments. We argue that both basic and applied research are essential to
accelerate the potential of AI models, and this is particularly so during a
rapidly evolving pandemic. This perspective on the response to COVID-19, may
provide a glimpse into how the global scientific community should react to
combat future disease outbreaks more effectively.Comment: Accepted in Nature Machine Intelligenc