Questioning the impact of AI and interdisciplinarity in science: Lessons from COVID-19

TL;DR

This paper critically examines the role of AI and interdisciplinarity in COVID-19 research, revealing that impact depends more on knowledge diversity than team interdisciplinarity.

Contribution

It demonstrates that the scientific impact of COVID-19 AI research is linked to knowledge diversity rather than team interdisciplinarity.

Findings

Impact correlates with knowledge diversity, not team interdisciplinarity.

Most COVID-19 AI papers have low visibility and impact.

Team structure influences successful integration of computational methods.

Abstract

Artificial intelligence (AI) has emerged as one of the most promising technologies to support COVID-19 research, with interdisciplinary collaborations between medical professionals and AI specialists being actively encouraged since the early stages of the pandemic. Yet, our analysis of more than 10,000 papers at the intersection of COVID-19 and AI suggest that these collaborations have largely resulted in science of low visibility and impact. We show that scientific impact was not determined by the overall interdisciplinarity of author teams, but rather by the diversity of knowledge they actually harnessed in their research. Our results provide insights into the ways in which team and knowledge structure may influence the successful integration of new computational technologies in the sciences.