The challenges of deploying artificial intelligence models in a rapidly evolving pandemic

TL;DR

This paper examines the limited impact of AI models in managing COVID-19, analyzing challenges in deployment and emphasizing the need for adaptable research to improve future pandemic responses.

Contribution

It identifies key barriers to AI adoption in COVID-19 healthcare applications and highlights the importance of translating models to local contexts during rapidly evolving crises.

Findings

AI has had limited impact in COVID-19 management so far

Challenges include clinical needs and local healthcare environment adaptation

Both basic and applied research are crucial for future pandemic AI deployment

Abstract

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.