Artificial Intelligence (AI) is the discipline that seeks to develop technologies that are able to adapt their behaviour in response to different situations, thus imitating the flexibility and general-purpose nature of human intelligence (Markoff, 2016; Mitchell, 2019; Wooldridge, 2020).
In recent years, the field has adopted a machine learning paradigm where the AI systems it develops ‘learn’ patterns from examples (generally big datasets labelled with features of interest) or by exploring simulated environments (Russell & Norvig, 2016; Sutton & Barto, 2018). This is underpinned by deep learning algorithms able to extract structure from complex data such as images, video, sound, language and networks to make accurate predictions (Goodfellow et al., 2016).
These techniques have contributed to important advances in computer vision, natural language processing, robotics and scientific research and development (R&D), leading economists to recognise AI as a general purpose technology with transformational potential (Cockburn et al., 2018; Klinger et al., 2018). This has been accompanied by growing levels of funding, investment and public, and policy interest in these powerful technologies.
AI systems are also expected to make important contributions across the health landscape, improving drug discovery, disease prevention, diagnosis and treatment and operational efficiency (Mateos-Garcia, 2019; Ravì et al., 2016; E. Topol, 2019; E. J. Topol, 2019). In recent months, researchers have identified several opportunities to apply AI or machine learning to tackle the COVID-19 pandemic, with a recent review showcasing examples of these applications in the molecular, clinical and social domains (Bullock et al., 2020; Naudé, 2020a; van der Schaar & Alaa, 2020).
In this paper, we build on these reviews to quantitatively map the levels of AI research activity to tackle COVID-19 using data from three widely used preprints repositories – arXiv, bioRxiv and medRxiv. Critically, in addition to measuring the volume of AI research to tackle COVID-19 and its evolution in recent months, we also use various data science techniques to decompose the field into application areas where AI systems are being developed or deployed to tackle COVID-19. We also analyse the geography of AI research and the knowledge base on which it builds, and study the trajectory of the research teams working in the field.
This way, we aim to get a more granular understanding of the AI response to COVID-19 and some of its potential limitations. This is informed by the idea that, despite their generality, modern AI techniques underperform in the absence of big datasets or where data exists but cannot be used for analysis, for example for privacy reasons, and that the predictions they generate are often difficult to explain and interpret, rendering them less useful for high-stakes health decisions where accountability is key (Marcus, 2018). Researchers and journalists who are assessing potential pitfalls in the AI response to COVID-19 have already pointed at these issues (Bullock et al., 2020; Naudé, 2020b). Through our analysis of the composition of research in this area, and how it compares to broader research related to COVID-19, we aim to evidence the situation quantitatively.
Our analysis also connects with an emerging body of work about the response of the research and innovation (R&I) system to COVID-19. This work highlights the velocity with which research communities from many different disciplines have reoriented their efforts in a collective mission to tackle COVID-19. Their strong problem-focus may encourage interdisciplinary collaboration, help deploy technologies such as AI in the health sector and bring new actors into the R&I system (Younes et al., 2020).
At the same time, some scholars have raised concerns about the consequences of this COVID-19 ‘research rush’. Research during this period may have lower standards of review, which could result in low-quality and even risky research being published; a focus on short-term research agendas by fast-moving players, which may discourage entry by other organisations with longer-term goals and more rigorous methods and cultures (Bryan et al., 2020); and contributions to the debate that are not sufficiently informed from researchers without expertise in relevant fields such as epidemiology. We seek to evidence these issues through our analyses of the quality of AI research to tackle COVID-19 (which we proxy through citations) and the track record of participants in the field.
By looking at the knowledge base of AI research to tackle COVID-19, we also attempt to measure the extent to which AI researchers are incorporating knowledge from other disciplines in their work or not. In doing this, we build on previous studies suggesting that AI research tends to be disengaged with other research fields (Frank et al., 2019).
The code and data underpinning the paper can be accessed from this GitHub repository.
