Trends in the use of AI in science : a bibliometric analysis.
Date: 2023-06-09
Institutions: Directorate-General for Research and Innovation
Subjects: China, EU Member State, EU research policy, United States, applied sciences, artificial intelligence, productivity, scientific discovery, strategic autonomy

 
 
 
 
 
Trends in the use of AI in science 

European Commission 
Directorate-General for Research and Innovation 
Directorate G — Common Policy Centre 
Unit G.1— Common R&I strategy and foresight service 

Contact   Alexandr HOBZA 
Email  

RTD-ECONOMIC-ANALYSIS@ec.europa.eu 
RTD-PUBLICATIONS@ec.europa.eu 

European Commission 
B-1049 Brussels 

Manuscript completed in March 2023. 

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EUROPEAN COMMISSION 

Trends in the use of AI  
in science 

A bibliometric analysis 

2023 

                   Directorate-General for Research and Innovation                                   EN

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
TABLE OF CONTENTS 

ABSTRACT ......................................................................................... 3 

1.  Introduction .............................................................................................. 3 

2.  The potential benefits of AI in specific scientific domains. Measuring 

the use of AI in science. ........................................................................... 4 

2.1 

2.2 

2.3 

Quantifying the impact of AI 

The use of AI in science 

Approach taken in this paper 

4 

7 

9 

3.  AI in science: global trends and international benchmarking .................... 10 

3.1 

3.2 

Global trends of AI in science 

The EU’s global position in AI scientific production 

10 

12 

4.  The EU research landscape in AI in science ............................................ 18 

5.  Conclusions .............................................................................................. 25 

6.  References ............................................................................................... 27 

7.  Annex: Methodological note ..................................................................... 31 

7.1 

7.2 

Definitions of AI 

Data collection methodology 

31 

31 

2 

 
 
ABSTRACT 

Artificial  intelligence  (AI)  has  the  capability  of  boosting  scientific  productivity, 
augmenting human cognitive capabilities, and accelerating breakthroughs. Given 
the  magnitude  of  this  potential,  efforts  have  increasingly  been  focused  on 
tracking  the  diffusion  and  impact  of  AI  in  science  and  innovation.  This  paper 
provides  new  insights  into  the  role  of  AI  in  scientific  research  across  various 
domains of science. It is evident that AI as applied to science and research has 
been growing at a significant pace in recent years, with China  leading the way, 
followed  by  the  EU  and  the  US.  Indeed,  the  EU’s  performance  is  found  to  be 
highly  heterogeneous  across  its  Member  States.  If  current  trends  persist,  the 
probability of future scientific discoveries being driven primarily by AI applications 
and  tools  is  set  to  increase  significantly.  Failure  to  keep  pace  with  the 
development and uptake of AI in science poses important challenges for the EU, 
including  the  need  to  address  concerns  about  strategic  autonomy,  trends  in 
scientific work and the labour market and, more broadly, productivity and growth. 

1. 

Introduction 

Artificial intelligence (AI) constitutes a ground-breaking suite of technologies 
with the potential to have a profound effect on all aspects of human existence. 
Its importance is escalating rapidly, in terms not only of the cutting-edge innovations 
being  made  but  also  of  the  prominence  it  has  gained  in  public  debates.  The 
development of large language models (LLMs) – in particular, since the launch of 
ChatGPT  at  the  end  of  2022  –  has  acted  as  a  catalyst  for  both  innovation  and 
competition  among  the  main  AI  players,  positioning  AI  technology  as  a  veritable 
“game changer” in scientific research. Moreover, given its general-purpose nature, 
AI has the ability to have a transversal impact on all sectors and is expected to play 
a critical role in the Twin – green and digital – Transition (EIB, 2021) and in achieving 
the Sustainable Development Goals (SDGs) (Vinuesa et al., 2020; Bianchini et al., 
2023).  

AI is rapidly becoming an indispensable tool in the scientific process. It already 
serves  as  a  robust  research  mechanism  for  many  scientists,  and  its  adoption  is 
expected to undergo rapid growth in the immediate future. Its capabilities underpin 
the  process  of  scientific  discovery  (Bianchini  et  al.,  2022;  Krenn,  2022)  and 
innovation (Rammer et al., 2022), playing, for example, a central role in processing 
large-scale  scientific  data  and  generating  patterns,  predictions,  and  models. 
Moreover,  it  facilitates  understanding  of  scientific  output  by  means  of  information 
retrieval,  natural  language  processing,  and  recommender  systems  in  extensive 
repositories  of  scientific  papers.  Indeed,  according  to  the  ‘AI  Index  Report  2023’ 
(Institute for Human-Centered AI, 2023), in 2022, AI played a significant role in areas 
as  diverse  as  plasma  control  in  nuclear  fusion,  algorithm  optimizations  for  matrix 
multiplications  (with  important  implications  for  efficient  computing)  and,  thanks  to 
generative AI models, the accelerated discovery of antibodies.  

AI has the potential to serve as a catalyst for scientific productivity, resulting 
in  more  efficient  outcomes  and  pushing  back  scientific  boundaries.  AI 

3 

 
 
increases human cognitive capacities, solving problems and expanding the limits of 
human  achievement  by  tackling  complex  processes,  such  as  data  analysis,  and 
producing  research  that  would  be  unattainable  using  traditional  tools  (e.g.,  the 
protein  folding  problem1).  Although  its  overall  impact  on  scientific  productivity 
remains unconfirmed, AI has the potential to shorten the typical timeframe needed 
for  scientific  discoveries  to  be  taken  up  and,  thus,  to  contribute  to  addressing 
pressing global challenges, especially at a time when scientific productivity seems 
to be stagnating and new ideas are “getting harder to find” (Bloom et al., 2020). 

In recent times, there has been an increasing effort to track the dissemination 
of AI within science and innovation, albeit with less focus on quantifying its 
impact.  Having  high-quality  data  and  metrics  to  monitor  the  development  and 
adoption of this emerging technology remains critical from both a policy and strategic 
perspective.  The  European  Commission  places  the  accelerated  adoption  of  AI 
technologies  at  the  heart  of  its  strategy  to  establish  EU  global  technological 
leadership,  and  the  2021  Coordinated  Plan  on  Artificial  Intelligence  lays  out  the 
actions to be undertaken by EU Member States to accelerate AI investments and 
reduce fragmentation within the EU Single Market (European Commission, 2021). 
Nevertheless, the process of mapping AI research faces two critical challenges: 
first,  establishing  a  precise  definition  of  AI,  and  second,  distinguishing  between 
fundamental AI research and its applications across various fields.  

The  aim  of  this  paper  is  to  add  on  the  existing  evidence  on  current  trends  of  AI 
applications, looking at the global trends of AI in science, with a focus on the EU’s 
performance vis-à-vis that of other big global innovators, and mapping the diffusion 
of AI technology in science and its application in different domains within the EU’s 
research landscape. 

The  remainder  of  the  paper  is  structured  as  follows.  Section  2  describes  the 
methodological approach adopted in the paper, building on the work of other studies 
in the relevant literature. Section 3 and 4 provide an overview of the main findings 
of the analysis. Section 5 concludes the paper. 

2. 

The  potential  benefits  of  AI  in  specific  scientific  domains. 
Measuring the use of AI in science. 

2.1  Quantifying the impact of AI 

AI lacks a universally accepted definition. Since its inception, the field has been 
associated with ever-shifting definitions (see Nilsson, 2009; and Russell and Norvig, 
2021, for comprehensive accounts of AI history): some place greater emphasis on 
the operational characteristics of intelligent machines; others prefer to focus on the 
objectives of AI research. Although the most recent definitions aim to be technically 
accurate, technology-neutral, and applicable to both short- and long-term horizons, 

1 See Jumper et al. (2021). See also https://www.science.org/doi/10.1126/science.370.6521.1144  

4 

 
 
 
AI spans multiple evolving, overlapping domains, making it difficult to identify  just 
what does and does not qualify as AI.2 

The diffusion of AI knowledge  is conducted via a mix of different channels. 
Most  AI  mapping  studies  rely  on  scientific  publications  and  patents  for  their  data 
sources,  although  AI  development  also  occurs  extensively  in  cloud-based 
repositories  (GitHub,  Hugging  Face,  Apache  Allura,  etc.)  as  well  as  on  blog 
platforms (New Scientist, The Conversation, WIRED, etc.).  

Bibliometric analyses of publications face specific constraints and difficulties. 
Here,  we  focus  solely  on  scientific  publications  which,  despite  their  limitations, 
provide  extremely  valuable  information  about  the  advances  occurring  within  and 
across all fields of science. In general, because of the absence of an accepted AI 
ontology,  three  main  approaches  have  emerged  in  the  identification  of  AI-related 
scholarly activity, namely: (i) a predefined keyword approach; (ii) machine learning 
approach(es); and (iii) a combination of the two, which allows words to be added to 
an initial list. 

i. 

ii. 

The first approach is relatively straightforward. It involves searching for a set 
of ad hoc and predefined terms in a publication’s title, abstract, keywords, 
and, where feasible, the entire body of the document (e.g., Cockburn et al., 
2018; Tran et al., 2019; Guo et al., 2020; De la Vega Hernández et al., 2023). 
Relying on a list of search terms for document retrieval is common practice 
in research on emerging technologies and sciences. However, because of 
the challenge of defining just what AI is exactly, there is no definitive, “ready-
to-use” list of terms that might be deemed authoritative for this purpose. 
In selecting terms for inclusion on the list, a researcher can either adopt a 
highly  parsimonious  approach  and  consider  only  a  limited  number  of 
essential terms, or opt for a broader approach, resulting in a more extensive 
list (e.g., Gargiulo et al., 2022). Nevertheless, the essential terms selected 
typically include general keywords such as ‘Artificial Intelligence’, ‘Machine 
Learning’,  and  ‘Deep  Learning’,  among  others.  It  should  be  stressed  that 
these terms capture the main bulk of scientific activity – as shown below in 
this  study  (see  Table  1A  in  Annex).  Moreover,  it  is  not  uncommon  to 
encounter various terms that refer to specific machine learning techniques, 
such as ‘Random Forest’, ‘Decision Tree’, and ‘Boosting’. 
The  second  approach  –  often  referred  to  as  “using  AI  to  define  AI”  – 
leverages certain machine learning (ML) techniques to analyse publication 
abstracts  (e.g.,  Klinger  et  al.,  2021;  Bianchini  et  al.,  2022).  In  this  way, 
search terms are not defined ex ante, but, instead, are “learned” directly from 
the data. A common strategy here is to use embedding algorithms, such as 
Word2Vec and its variants, which map the corpus from a high- into a lower-
dimensional space, while preserving semantic relationships, thus making it 
easier to cluster words of similar meaning or from similar contexts. Once the 

2 See the methodological annex at the end of this paper for the most recent definitions of AI. 

5 

 
 
 
 
iii. 

model  has  been  trained  on  a  large  sample  of  publications  (e.g.,  arXiv  or 
PubMed), it can be queried to obtain a set of words that fall within the cluster 
that includes the term ‘Artificial Intelligence’ or the like. Thus, a list of search 
terms can be generated and used to identify AI-related documents. 
While this approach is appealing, it is often more pragmatic than scientific, 
inasmuch as it requires the making of many, often iterative, decisions, based 
on trial and error. These decisions include, for instance, text input cleaning 
(e.g.,  tokenization,  stop-word  removal,  stemming,  and  lemmatization), 
setting hyperparameters (e.g., embedding size, learning rate), and several 
post-processing operations (e.g., assembling word embeddings, clustering). 
Interestingly,  previous  research  using  natural  language  processing-based 
approaches typically ended up with virtually the same search terms as those 
defined beforehand by experts. 
The  third  approach  –  i.e.,  keywords  plus  ML  –  consists  generally  of  two 
steps. First, keywords present in publications across all journals are tagged 
as  AI-related  (e.g.,  the  “All  Science  Journal  Classification”  in  Scopus  and 
“Subject  Categories”  in  Web  of  Science),  and,  second,  the  set  is  then 
augmented  and  refined  using  text  mining  techniques  and  by  expert 
validation. Using this method, Baruffaldi et al. (2020), for example, were able 
to compile a list of 193 AI-related keywords and use them to identify over 2.4 
million documents in Scopus spanning the period 1996 to 2016.     
The same approach can be employed in reverse, that is, starting with a large 
list of keywords and concepts identified from various sources – including AI 
textbooks, news items, online blogs, and others – and then using ML tools 
to streamline and refine the list to a more manageable size. By adopting this 
method, for example, a team at Elsevier (2020) was able to identify more 
than 600,000 AI documents for the period 1998 to 2017. 

Regardless of the approach, various caveats should be borne in mind:  

•  First, some terms may be overly general and open to contention. The term 
‘Robot’  is  an  example  in  point:  while  some  robots  may  incorporate  AI 
technology  so as to enhance their functionality, others may  contain no AI 
components whatsoever (Russell and Norvig, 2021 – Ch.26). The term ‘Big 
Data’ is another example: although AI can be a powerful tool for extracting 
insights  and  patterns  from  big  data,  it  is  by  no  means  a  prerequisite  for 
working with big data (De Mauro et al., 2015).  

•  Second,  special  attention  needs  to  be  paid  to  certain  terms  that,  while 
significant in the field of AI, may not refer to it exclusively. For instance, the 
term  ‘Neural  Network’  is  potentially  problematic  inasmuch  as  some 
publications  using  this  term  may  not  necessarily  be  discussing  artificial 
intelligence, but rather human intelligence and the biological brain. Studies 
have  shown,  however,  that  this  problem  is  mainly  confined  to  the  field  of 
neuroscience  and  that  it  has  minimal  impact  in  other  scientific  disciplines 
(Bianchini et al., 2022 – Section 5).  

6 

 
 
 
•  Third,  as  AI  technology  becomes  increasingly  integrated  into  scientific 
research and permeates the scientific system, references to AI techniques 
and tools in the titles, abstracts, and keywords of scientific publications may 
no  longer  be  a  reliable  method  for  identifying  AI-related  scientific  activity. 
This  means  novel  approaches,  based  on  the  comprehensive  content 
analysis of full texts, will need to be developed. 

2.2 

The use of AI in science 

A major challenge in mapping the diffusion of AI in the sciences is differentiating 
between research that develops AI technology – i.e., fundamental or basic AI – and 
research that uses AI to solve field-specific scientific problems – i.e., applied AI. To 
illustrate this, consider two papers: the first aims to train more efficiently a neural 
network architecture, while the second applies the same architecture to detect, say, 
cancer  from  an  MRI  image.  While  the  former  would  be  categorized  as  basic  AI 
research, the latter is clearly an example of applied AI research in the medical field.  

However,  the  line  between  basic  AI  and  its  applications  can,  at  times,  be  quite 
blurred, given just how interconnected theoretical and applied studies can be. On 
the one hand, advances in basic AI research can lead to the development of more 
effective  and  more  efficient  AI  tools  that  can  be  applied  to  a  range  of  scientific 
problems, while, on the other, feedback from the application of AI in specific scientific 
settings  can  help  identify  areas  where  basic  AI  research  can  be  improved  or 
expanded. 

Over time, the focus of basic AI research has shifted from  an initial emphasis on 
neuroscience and philosophy to a more computer science-oriented and – albeit to a 
lesser extent – mathematical approach. Bibliographic studies of the period 1950 to 
2017  confirm  that  AI  has  transitioned  towards  computational  research  in  recent 
decades, especially with the emergence of deep learning techniques (Frank et al., 
2019). Thus, earlier studies tend to consider that AI publications in all areas other 
than computer science represent applications of AI techniques designed to address 
field-specific research problems (e.g., Cockburn et al., 2018; Bianchini et al., 2022). 
In what follows, we also opt to adhere to this approach (see Table 2A in Annex).   

Box 1: Applications of AI in the sciences3 

AI has a vast array of potential applications that span a continuum between the 
two extremes of search and discovery. At the search end of the spectrum, AI can 
support access to knowledge and information, especially at times characterized 
by the explosion of data and information; at the discovery end of the spectrum, 
often  as  the  end-result  of  a  research  project,  AI  can  be  employed  to  identify 
patterns  in  data  in  an  open-ended  manner,  leading  to  new  discoveries  and 
insights  (Agrawal  et  al.,  2018;  Raghu  and  Schmidt,  2020;  Xu  et  al.,  2021, 

3 Note that the examples considered constitute a non-exhaustive list of potential applications of AI in science. 

7 

 
 
 
Bianchini et al., 2022). Below, we consider some of the most common applications 
of AI in the scientific pipeline. 

The most common use of AI in science is to address complex prediction problems 
– that is, mapping inputs to predicted outputs. The problems can be of any kind, 
as can the type of methodological approach adopted. For instance, convolutional 
neural networks (CNNs) can be used to process MRI images and to predict the 
possible presence of cancer. Examples of the many computer vision tasks include 
semantic segmentation, where the goal is to categorize pixels according to the 
high-level group to which they belong, and pose estimation, where the goal is to 
predict and track the location of  a person or object. Other techniques, such as 
recurrent neural networks (RNNs), are common in scientific applications involving 
the prediction of sequential structures, such as in genomics and proteomics, but 
also in finance. 

A second common application of AI is to perform transformations of input data, 
including  dimensionality  reduction,  clustering,  data  augmentation,  and  image 
super-resolution, to name but a few. Dimensionality reduction and clustering are 
simple but effective methods for revealing hidden properties in data and are often 
the first step in exploring and visualizing data, before any other prediction tasks 
are undertaken. Image super-resolution and data compression are other common 
applications that can facilitate data analysis and enable the researcher to save 
and optimize space. 

A  third  application  is  the  optimal  parameterization  of  complex  systems.  Here, 
techniques such as reinforcement learning can be used to search for the optimal 
set  of  parameters  that  maximize  or  minimize  a  specific  objective  function  or 
produce a desired outcome. A recent example is the configuration of tokamaks 
(for  nuclear  fusion)  with  deep  reinforcement  learning,  which  has  enabled 
scientists to model and maintain a high-temperature plasma within the tokamak 
vessel, a problem that had hitherto proved impossible to solve (Delgrave et al., 
2022). 

Another  valuable  application  of  AI  is  automating  (or  partially  automating)  the 
literature  review  process,  which  can  be  facilitated  by  powerful  search  engines 
based on LLMs. Platforms like Elicit and Perplexity work through a chatbot-style 
interface,  enabling  researchers  to  interact  dynamically  with  the  machine.  The 
researcher  can  initiate  a  conversation  to  search  for  information  about  past 
research in a certain area and receive a summary of key information about that 
field. The newest tools can even remember the conversational context, improving 
the quality of the exchange between user and machine. Most of these AI-powered 
platforms  offer  other 
in 
brainstorming research questions and directions – i.e., rephrasing their research 
questions and suggesting potential research directions based on the current state 
of the art – and providing suggestions on how to improve prose writing and editing. 

functionalities,  such  as  assisting 

researchers 

Still within the context of academic literature reviews, an interesting application is 
literature-based  discovery,  where  AI  can  uncover  implicit,  hidden  associations 
from  existing  studies,  resulting  in  interesting,  surprising,  non-trivial  hypotheses 

8 

 
 
that are worth studying. Machine reading comprehension systems are particularly 
useful  in  this  context,  as  they  can  identify  gaps  in  the  literature  and  propose 
variations on existing experiments. 

Finally,  AI  or,  specifically,  simple  robotics  can  be  used  to  automate  tedious, 
routine laboratory tasks such as media and buffer preparation or pipetting. These 
tasks require a high degree of accuracy but have relatively low value-added. 

2.3  Approach taken in this paper 

The publication data used in this paper are drawn from the Web of Science 
(WoS)  Core  Collection.4  We  are  specifically  concerned  with  scholarly  activity 
related to AI in the period 2000 to 2021 (data for 2022 were incomplete and not fully 
comparable with those of previous years at the time of extraction). Relevant papers 
are identified using a list of AI-related keywords and these studies are classified into 
different fields in line with established practices in the field, albeit with a number of 
specific adjustments. 

Publications are assigned to a country and to a specific domain of science. 
The link between a paper and a country is established based on the affiliations of 
the  authors,  taking  a  “weighted-by-author"  approach,  to  avoid  double  counting 
issues  (i.e.,  assigning  one  paper  to  different  countries).  Papers  are  originally 
classified in more than 250 different WoS categories. This granular categorization is 
consolidated  in  21  macro-categories  (domains).  A  paper  can  be  associated  with 
more than one domain, which means there could be some level of double-counting 
in the analysis per scientific domain.  

When  analysing  the  applications  of  AI  in  science,  we  exclude  papers  that 
belong exclusively to the domain of ‘Computer Science’. In the classification per 
domain, we distinguish between the field of ‘Computer Science’ and all other fields. 
When a paper is exclusively assigned to areas within the computer sciences, we 
consider this paper as belonging to the development of AI and close applications, 
which we  refer  to  as  “Core  AI”.  Papers  that are  classified in  at least  one domain 
other than that of computer science are considered as belonging to applications of 
AI in science.  

More details on the methodology are provided in the methodological annex at the 
end of this paper. 

4 https://www.webofscience.com/wos/woscc/basic-search  

9 

 
 
 
 
 
3.  AI in science: global trends and international benchmarking 

3.1  Global trends of AI in science 

The field of AI is growing at a faster rate than that of scientific production as 
a  whole.  In  general,  global  scientific  activity  has  grown  at  around  5%  per  year 
between 2004 and 2021. In the same period, the annual growth rate of AI-related 
publications has been around or above 15%, except for the years between 2010 and 
2012, when scientific production in the field of AI stagnated (Figure 1), presumably 
because of the reorientation in research priorities and funding linked to the onset of 
the financial crisis. The slowdown observed since 2019 is presumably attributable 
to the effects of the COVID-19 pandemic.  

Figure 1. Growth in scientific activity (3-year average yearly growth)  

Source:  European  Commission,  DG  Research  &  Innovation,  calculations  based  on  Web  of 
Science data. Annual growth calculated as a 3-year rolling average. 

Scientific activity related to AI applications accounts for a significant share of 
total  publications  in  the  field  of  AI.  Between  2000  and  2021,  the  evolution 
undergone by the total number of AI publications and publications related exclusively 
to  AI  applications  in  science  followed  a  similar  pattern  of  growth  (Figure  2  –  left 
panel), with the latter accounting for around 50% of total AI publications up to 2018. 
Since  that  date,  there  has  been  a  significant  increase  in  this  share,  indicating  a 
decoupling of AI applications in science from the overall growth  in the field of AI. 
This indicates that AI applications in science are growing faster than the AI field 
as a whole (Figure 2 – right panel). 

10 

 
 
 
 
Figure 2. Number of publications (left) and share of publications on AI 
applications in science (right) 

Source: European Commission, DG Research & Innovation, calculations based on Web of 
Science data 

The impact of AI could vary considerably across different scientific domains. 
Over the last 5 years, applied sciences (such as, engineering and materials), as well 
as the natural and life sciences are the fields that have reported the highest number 
of publications on AI applications. The social sciences, including economics and the 
humanities, account for a lower share of publications in which AI is used as a tool 
(Figure 3). In terms of growth, the material sciences is the discipline with the highest 
growth  rate  (almost  50%  on  a  yearly  basis),  followed  by  medicine  (clinical  and 
general – around 45%). Surprisingly, the neurosciences, one of the disciplines that 
served as a reference for the development of AI, presents one of the lowest growth 
rates (22%), while the lowest is found in the discipline of art and literature (5%). 

Figure 3. Number of publications (2017–2021) on AI per scientific domain 

11 

 
 
 
 
 
Source:  European  Commission,  DG  Research  &  Innovation,  calculations  based  on  Web  of 
Science data 

3.2 

The EU’s global position in AI scientific production 

China, the US and the EU are global leaders in AI scientific production and in 
publications related to AI applications in science. More specifically, the EU and 
the US have reported similar levels of AI publications over the last few decades, with 
the  EU  showing  a  modest  advantage  up  to  2017,  before  the  trend  was  slightly 
reversed in the US’s favour. Particularly impressive has been the performance of 
China, which entered the 21st century lagging behind its main competitors, but it has 
been able to catch up quickly and, since 2017, has outperformed both the US and 
the EU (Figure 4 – left panel). A similar trend is also observed if we focus solely on 
the  share  of  publications  related  to  AI  applications,  with  China  reporting  a 
remarkable  growth  at  the  expense  of  the  other  two  economies  (Figure 4  –  right 
panel).  

Figure 4. Number of publications (left), and relative share of publications of 
the EU, US, and China (right), on AI applications in science 

Source: European Commission, DG Research & Innovation, calculations based on Web of 
Science data 

The number of publications dedicated to AI applications in science has grown 
at a faster rate since 2017. The period between 2017 and 2021 has witnessed a 
marked acceleration in the pace of publications. This is particularly noteworthy in the 
case of China, which reported an average yearly growth rate of 39%, followed by 
the US (36%) and the EU (32%) (Figure 5 – left panel). If these growth rates remain 
unchanged over the next four years, the gap between China and the EU will widen 
further (Figure 5 – right panel), and EU’s publications will represent less than 60% 
of Chinese production. 

Figure  5.  Average  yearly  growth  by  period  (left),  and  projected  number  of 
publications (right), on AI applications in science 

12 

 
 
 
Note: the projections for 2021 are calculated applying the yearly growth rate from 2017–2021 for 
each of the countries/region. 
Source: European Commission, DG Research & Innovation, calculations based on Web of 
Science data. 

However, the widening gap between China and its counterparts is narrowed 
when accounting for the quality of publications. Figure 6 shows the trend in the 
number  of  publications  on  AI  applications  in  science,  with  at  least  one  citation 
(Figure 6 – left panel). When the quality of publications is accounted for, the overall 
performance of the three main innovators decreases compared to the overall trend 
reported in Figure 5. However, this drop in performance is more significant in the 
case of China than it is for the EU and the US, as the gap between the Chinese 
performance and that of its counterparts becomes narrower.  

Yet, the share of Chinese publications with zero citations has fallen in recent 
years. Indeed, the share of Chinese publications reporting at least one citation has 
converged with that of both the US and the EU (with the former leading the way)5 
(Figure 6 – right panel), signalling that the quality of Chinese scientific production in 
AI applications has improved. 

Figure 6. Number of publications (left), and share of publications (right) with 
at least one citation, on AI applications in science   

Source:  European  Commission,  DG  Research  &  Innovation,  calculations  based  on  Web  of 
Science data 

5 Data for 2021 have been excluded as the share of papers without citations increases as we approach the 

data extraction cut-off date.  

13 

 
 
 
 
 
The distance between the three main players narrows further when focusing 
solely on top publications (Figure 7). If we consider the top 10% of publications 
on AI in science by number of citations6, China only reached the head of the rankings 
in  2019,  replacing  the  US  in  a  role  it  had  held  for  the  previous  two  decades. 
Interestingly, the gap in high quality scientific production between the latter and the 
EU has also been reduced over time, with the performance of the EU progressively 
converging towards that of the US. 

Figure 7. Number of top publications (top 10% of publications by number of 
citations) on AI applications in science 

Source:  European  Commission,  DG  Research  &  Innovation,  calculations  based  on  Web  of 
Science data 

Beyond the three main players, the major performers in scientific activity related 
to  AI  applications  include  India,  the  UK,  South  Korea,  and  Japan.  The 
publication  performance  of  India  underwent  an  acceleration  around  2011–2012, 
followed by that of the UK and South Korea. Growth in the latter became significant 
in 2017, with an annual growth rate of 53% for the period 2017–2021 (a much higher 
rate than that reported by the rest of the advanced economies analysed), followed 
by Japan with a yearly growth of 37%. Meanwhile, India’s publication performance 
has  progressively  slowed  since  2017,  reporting  a  yearly  growth  rate  of  25% 
thereafter.  

6 We also considered weighted citation, but found no significant differences in the trend. 

14 

 
 
 
 
 
Figure  8.  Number  of  publications,  for  a  selection  of  countries,  on  AI 
applications in science 

Source:  European  Commission,  DG  Research  &  Innovation,  calculations  based  on  Web  of 
Science data 

Much  of  the  gap  in  scientific  production  between  China  and  the  EU  is 
attributable  to  China’s  pre-eminence  in  the  scientific  domains  in  which  AI 
plays  a  more  prominent  role.  These  include  engineering,  geosciences,  and 
physics  and  mathematics  where,  between  2017  and  2021,  China  recorded  the 
highest  number  of  publications  (Figure  9).  The  US  heads  the  rankings  in  health-
related domains, while the EU leads the field in social sciences and humanities, but 
lags behind in all the other major scientific domains. 

15 

 
 
 
 
Figure 9. Number of AI publications (2017-2021) per domain of science

Source: European Commission, DG Research & Innovation, calculations based on Web of 
Science data 

Additionally, Chinese production has grown significantly in all three of the main 
areas  of  AI  applications  (i.e.,  biomedicine,  engineering,  and  geosciences)  over 
the  last  5  years,  further  consolidating  its  leadership  in  these  domains. 
However, it is in the neurosciences that China has shown strongest growth, pulling 
further  and  further away  from  both  the  EU  and  the  US.  The  EU, meanwhile,  has 
undergone fastest growth in the domains of physics, mathematics, and chemistry 
(Figure 10).  

16 

 
 
 
 
Figure 10. Average yearly growth rate (2017–2021) in the use of AI per domain 
of science (for a selection of domains) 

Source:  European  Commission,  DG  Research  &  Innovation,  calculations  based  on  Web  of 
Science data 

In terms of quality, all the main scientific domains are characterized by shares 
of publications with citations of between 70 and 80%, with the same incidence 
across China, the US, and the EU. Only the social sciences and humanities report 
a lower number of publications with citations (around 60%), with China presenting a 
broader gap with respect to this domain than that presented by either the US or the 
EU.  

Box  2:  An  examination  of  the  high-impact  sectors  flagged  up  in  the  ‘AI 
Coordinated Plan’ 

The Coordinated Plan (European Commission, 2021) identifies a set of sectors in 
which the EU should be striving to build strategic leadership.7 Among these areas 
of  action,  there  are  four  whose  publications  can  be  usefully  analysed  from  a 
bibliometric perspective: Agriculture, Environment, Health, and Transport. In this 
way,  a  picture  can  be  approximated  of  the  state  of  play  in  these  high-impact 
sectors.  

To undertake such an analysis, we opted to examine the level of classification of 
publications in the Web of Science  at a more granular level  and to establish a 
number of synthetic categories that might proxy these sectors. The sectors, as 
shown in Figure 2.1, differ markedly in their adoption of AI. Thus, it is evident that 
the Health and Environment sectors make much greater use of AI than the other 

7 https://digital-strategy.ec.europa.eu/en/policies/build-leadership-ai  

17 

 
 
 
 
 
two and that, among the three main players, the EU ranks second in each, the US 
leading the way in Health and China in the other three sectors.  

Figure 2.1. AI publications in selected sectors (2017-2021)  

Source:  European  Commission,  DG  Research  &  Innovation,  calculations  based  on  Web  of 
Science data 

4. 

The EU research landscape in AI in science 

Among  the  EU  Member  States,  Germany,  Italy,  Spain,  and  France  are  the 
leading producers of publications related to AI applied to science. In absolute 
numbers, these four countries are also the leading AI-publishing nations within the 
EU (Figure 11 – left panel). All four, with the exception of Spain, also report a yearly 
growth rate slightly above the EU average (Figure 11 – right panel). Among the rest 
of the Member States, Sweden and the Netherlands also report both a high absolute 
number of publications and a high growth rate, albeit that Estonia and Luxembourg 
report the highest growth, although these two countries both start from a significantly 
lower baseline in terms of the absolute number of publications.  

18 

 
 
 
Figure  11.  Number  of  publications  (left),  and  average  yearly  growth  in  the 
number of publications (right) for EU countries, 2017–2021, on AI applications 
in science  

Source:  European  Commission,  DG  Research  &  Innovation,  calculations  based  on  Web  of 
Science data 

The quality of publications on AI applications is quite heterogeneous across 
the Member States. Overall, the EU research landscape is characterized by a high 
incidence of publications on AI in science with no citations (Figure 12 – left panel),8 
standing at between 20 and 30% for the majority of countries. However, for some 
Eastern  European  countries,  such  as  Romania  and  Czechia,  the  share  is  even 
higher,  with  more  than  40%  of  their  AI-related  publications  in  science  failing  to 
receive a single citation. If we focus on the top 10% of the most cited papers (Figure 
12 – right panel), Germany  again leads the rankings, followed by Italy. France is 
third,  just  above  Spain,  with  the  Netherlands  ranked  just  below  them.  Sweden 
follows them in 6th position by number of publications in the top 10%, and 9th by 
total number of publications.   

8 Note that this incidence is especially high for 2021, the most recent data point in the dataset.  

19 

 
 
 
 
 
Figure 12. Total number of publications, and number of publications with at 
least 1 citation (left), and number of publications among the world’s top 10% 
cited papers (right) for EU countries, 2017-2021, on AI applications in science 

Source:  European  Commission,  DG  Research  &  Innovation,  calculations  based  on  Web  of 
Science data 

The  number  of  AI  publications  in  science  produced  by  a  country  strongly 
correlates  with  its  GDP.  While  this  correlation  might  be  expected,  GDP  is 
surprisingly  a  better  predictor  than  the  expenditure  on  R&D  (which  might  be 
assumed to be a more similar concept). As such, this correlation (shown in Figure 
13) can be used to detect countries with a performance above (the case of Italy and 
Spain) or below (the case of France) that expected from their GDP.  

20 

 
 
 
Figure 13. GDP (2017–2021) and total number of publications of EU Member 
States, 2017–2021, on AI applications in science 

Source:  European  Commission,  DG  Research  &  Innovation,  calculations  based  on  Web  of 
Science data and Eurostat [nama_10_gdp] 

Publication intensity provides a better metric for comparing the performance 
of the Member States. While absolute numbers of publications may not be readily 
interpretable  due  to  size  differences  between  the  EU  Member  States,  relative 
measures  –  i.e.,  the  number  of  publications  by  population  and  by  number  of 
researchers  –  can  be  more  insightful  (see  Figure  14).  These  two  measures 
complement  each  other,  given  that  the  number  of  researchers  can  also  differ 
notably. When applying both indicators, Cyprus is found to perform well. Among the 
largest Member States, scientific output by the number of researchers in Italy and 
Spain is exceptional, which explains in part their respective positions in relation to 
GDP.  

21 

 
 
 
Figure  14. Number of  publications  on AI  applications  in  science per  million 
inhabitants  (left),  and  per  thousand  researchers  (right),  for  EU  countries, 
2017–2021 

Source:  European  Commission,  DG  Research  &  Innovation,  calculations  based  on  Web  of 
Science data and Eurostat [rd_p_persage] 

Publications  on  AI  by  scientific  domain  follow  similar  patterns  to  that 
presented by the overall trend. The countries leading the publication rankings per 
domain  coincide,  in  most  cases,  with  those  reporting  the  highest  number  of 
publications  (Table  1),  with  Germany  leading  the  way  in  most  domains  (i.e.,  the 
biomedical sciences, engineering, geosciences, and also the neurosciences). While 
yearly  growth  rates  are  high  for  most  countries  and  most  domains,  a  few  fields 
present  very  high  growth  rates  (Table  2).  Typically,  the  highest  growth  rates  are 
found in  domains with  a medium  to low  number  of  overall  publications, while  the 
largest  domains  present  growth  rates  more  similar  to  those  of  the  overall  growth 
presented by publications on AI applications. The different growth rates per domain 
recorded in some countries may be indicative of the development of certain degrees 
of specialization in specific domains. 

22 

 
 
 
 
Table 1. Ranking of publications per scientific domain, EU countries, 2017–
2021

Note: Domains are ordered by total number of publications in the EU (2017–2021). 
Source:  European  Commission,  DG  Research  &  Innovation,  calculations  based  on  Web  of 
Science data 

Table 2. Yearly growth rates per scientific domain, in selected EU countries, 
2017–2021 

Note: Domains are ordered by total number of publications in the EU (2017–2021). Highlighted in 
green the top growth rate among the selected countries. 
Source:  European  Commission,  DG  Research  &  Innovation,  calculations  based  on  Web  of 
Science data 

23 

12345Biomedical Sciences GermanyItalyFranceSpainNetherlandsEngineering GermanyItalySpainFrancePolandGeosciences GermanyItalyFranceSpainNetherlandsPhysics and Mathematics GermanyItalySpainFrancePolandChemistry SpainGermanyItalyPolandFranceNeurosciences GermanyItalyNetherlandsFranceSpainEnvironmental Sciences SpainItalyGermanyFrancePolandClinical Medicine GermanyFranceNetherlandsItalySpainGeneral Medicine and Public Health GermanyItalySpainFranceNetherlandsMaterial Sciences GermanySpainItalyPolandFranceEconomics, Management, and Finance GermanyItalyRomaniaCzech RepublicSpainEducation and Information SpainGermanyItalyRomaniaPortugalLanguage and Culture GermanySpainFranceItalyNetherlandsRegional and Urban Planning GermanyItalySpainFranceNetherlandsAgriculture SpainGermanyItalyFranceNetherlandsSocial Science, Philosophy, and Religion GermanyItalyNetherlandsSpainFranceEcology GermanyItalyFranceSpainPortugalInfectious Diseases GermanyFranceItalySpainNetherlandsHistory, Politics, and Law GermanyItalyNetherlandsSpainBelgiumArt and Literature ItalyGermanySpainDenmarkFranceRankingGermanyItalyFranceSpainNetherlandsPolandBiomedical Sciences 35.833.431.428.032.422.1Engineering 33.232.533.330.148.622.5Physics and Mathematics 23.725.721.235.219.550.7Geosciences 30.132.845.829.725.562.0Chemistry 29.336.212.847.720.230.9Neurosciences 58.637.744.48.087.55.6Environmental Sciences 52.558.997.838.758.269.5Clinical Medicine 61.845.665.510.666.043.7General Medicine and Public Health 55.033.754.535.973.95.0Material Sciences 28.159.014.660.8-3.482.5Economics, Management, and Finance 29.532.937.248.265.714.5Education and Information 21.36.117.720.636.012.3Regional and Urban Planning 41.38.626.714.738.9-27.0Agriculture 31.548.726.918.123.267.5Language and Culture 32.76.631.522.349.5-11.9Ecology 31.316.051.965.813.214.2Social Science, Philosophy, and Religion 32.756.41.733.968.812.2Infectious Diseases 47.652.830.368.366.764.8History, Politics, and Law 42.012.6-17.19.842.82.8Art and Literature 38.57.538.746.7-18.5 
 
 
 
 
 
 
 
 
 
 
 
 
Box 3: Applications of AI in science vs “Core AI” 

Technology  development  is  fundamental  in  the  development  of  applications  of 
that technology. In the case of AI, the core of its development, and many of its 
applications, lie in the domain of Computer Science. For this reason, we would 
expect  to  find  a  strong  correlation  between  the  publications  exclusive  to  the 
domain of Computer Science and those that fall within other scientific domains. 
However, some traits might manifest themselves at the national level, with some 
countries  focusing  their  efforts  more  on  developing  “core”  technology  and  its 
applications, while other countries are more active in developing AI applications 
in science. 

Figure 3.1: Number of publications by EU countries, 2017–2021 

Source:  European  Commission,  DG  Research  &  Innovation,  calculations  based  on  Web  of 
Science data 

This strong correlation is more than apparent in the case of the EU Member States 
(Figure 3.1). Yet this mapping does not allow us to draw any inferences about the 
direction of the causal link between the two. That is, does the development of the 
technology facilitate its applications, or does the need to apply it in different fields 

24 

 
 
 
 
act as a stimulus for the development of the technology? Answering this question 
remains  critical  for  policymakers  in  their  efforts  to  understand  the  dynamics 
underpinning  this  field  of  science,  and  there  is  an  obvious  need  for  further 
research on this topic. 

5. 

Conclusions 

AI has an enormous potential to further advances in science and technology. 
Indeed, AI constitutes a powerful tool that is capable of inducing  positive change 
across a broad spectrum of fields, thanks to its ability to allow humans to achieve 
more,  and  at  a  faster  pace,  by  enhancing  existing  skills.  This  means  that  AI 
technologies  are  set  to  play  a  fundamental  role  in  increasing  the  efficiency  of 
scientific and innovation actions aimed at solving complex global challenges. 

The  applications  of  AI  in  science  and  research  have  grown  at  a  significant 
pace in recent years. At the global level, China is the most productive, in terms of 
both the absolute number of publications and the growth rate of this output, followed 
by  other  key  players,  most  notably,  the  EU  and  the  US.  Although  the  Chinese 
advantage is mitigated, in part, when considering indicators of research quality, its 
leadership  in  the  development  of  AI  and  in  its  applications  in  science  remains 
significant. 

Based on the evidence presented in this paper, the gap between the EU and China 
in this regard can be expected to increase in the future. The EU led the way in 
the application of AI in science up to 2017, when it was  overtaken  by China. EU 
production is currently at a similar level to that of the US but it presents a somewhat 
slower  growth  rate.  If  this  trend  is  confirmed,  the  window  available  for  the  EU  to 
catch up with its competitors is likely to shrink further in the future.  

Current  trends  highlight  the  need  for  actions  that  can  strengthen  the  EU’s 
position in the application of AI in research and scientific activities. Given its 
multiple applications across a range of fields, AI is one of the digital technologies 
with  the  greatest  potential  to  boost  EU  productivity,  and  to  revitalize  the  green 
transition and, in this way, increase EU competitiveness. Moreover, if future scientific 
discoveries are likely to be driven in the main by AI applications and tools, lagging 
behind in the development and uptake of AI in science poses major challenges for 
the  EU’s  strategic  autonomy,  increasing  the  risk  of  developing  dependencies  in 
strategic scientific fields.  

From a policy perspective, creating the right conditions to facilitate the uptake 
of AI across all scientific fields is no easy task. Based on the results of this paper, 
strengthening both the EU’s R&I ecosystems, as well as those of its Member States, 
to  facilitate  the  further  adoption  of  AI  in  science,  is  a  key  issue.  Moreover,  AI 
technologies  introduce  a  series  of  broader  challenges  that  policymakers  have  to 
address. Here, one of the most significant impacts to take into consideration is the 
impact  of  AI  on  jobs  (including  those  in  the  domains  of  science).  All  changes  in 
technology  invariably  lead  to  disruptions  and  threaten  to  exacerbate  existing 

25 

 
 
disparities. AI is no exception and particular attention will have to be paid to boost 
the  uptake  of  AI  technologies,  while  respecting  human  rights  and  preserving  the 
value of human endeavour and intellect, in line with a human-centric, transparent 
approach that promotes public trust in this field. 

Improving understanding of this technology at every stakeholder level is also 
critical. To achieve this, current tools and systems need to be reimagined to help 
researchers, companies, and policymakers exploit the potential of AI to the full. This, 
likewise,  calls  for a  better  understanding  of  the  current  state  of  knowledge in  the 
field,  so  as  to  enable  an  effective  prioritization  of  research  at  the  boundaries  of 
knowledge. 

The increasing use of AI as a tool to advance science is accompanied by new 
challenges  and  dilemmas.  Being  a  novel  technology,  AI  is  the  cause  of  new 
concerns among scientists. The swift adoption curve, as described in this paper, will 
serve merely to intensify these fears. While some of these problems are known or, 
at least, expected, new dilemmas are likely to present themselves. A prime example 
of  the  challenges  faced  is  the  way  in  which  generative models,  especially  LLMs, 
might affect different aspects of the scientific process (Birhane et al, 2023; van Dis 
et al., 2023). The perils of misuse also exist, most notably in potentially hazardous 
areas such as drug discovery (Shankar and Zare, 2022). At a broader scale, AI could 
impact the scientific process by introducing bias (for instance, in literature reviews) 
or creating obstacles for the reproducibility and interpretability of results. 

It is, therefore, critical that steps be taken to improve the quality of available 
evidence when monitoring future trends and developments. In this regard, the 
relevance of the business sector is especially important. The interest shown in the 
application  of  AI  in  science  by  the  tech  giants,  having  large  technological  and 
financial power, (such as, Google, Microsoft, and Meta), and by their related start-
ups, could be a major  game changer in  this field. The role of corporations in this 
regard (albeit an issue not specifically examined herein) could be of great relevance 
too. For example, in the UK, a huge share (70%) of leading publications on AI are 
generated solely by DeepMind.9 Likewise, open science might well be impacted as 
the  result  of  increased  competition  between  companies,  since  this  would  have 
serious ramifications for openness in the uptake of ideas by the research community. 

Finally,  the  impact  of  AI  on  scientific  productivity  clearly  requires  further 
analysis and monitoring. Quantifying the current impact of AI on the productivity 
of science is no easy task. The fact that the number of scientific publications using 
AI is growing faster today than the overall number of publications can be interpreted 
as  an  initial  indication  of  the  potential  of  AI  to  boost  research  productivity.  The 
anecdotal evidence of projects such as AlphaFold10 moreover points in this same 
direction. Results from this specific project highlighted enormous productivity gains 

9 The UK’s share of citations in the top 100 recent AI papers stands at 7.8% but once DeepMind’s contributions 
are removed, it falls to 1.9%: https://www.ft.com/content/470e9848-b2dd-4ad5-94cb-65e95c226545  
10 AlphaFold, with an interdisciplinary team of around 20 scientists, was able to predict the structure  of 200 
million proteins in a period of between 5 to 6 years. Before this, predicting just one structure could be the 
work of a significant part of a PhD thesis. 

26 

 
 
 
in the scientific process, and opened up, at the same time, multiple additional lines 
of research in medicine and drug discovery.11 This is an excellent example of the 
potential  of  AI  tools.  However,  further  research  is  needed  to  quantify  the  actual 
impact  of  AI  and  its  evolution,  taking  into  account  other  criteria  beyond  that  of 
scientific production (e.g., novelty, originality, and disruptiveness).  

6. 

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30 

 
 
 
 
 
7. 

Annex: Methodological note 

7.1  Definitions of AI 

Today, most definitions of AI centre on the concept of emulating intelligent behaviour 
through  machines,  where  intelligence  refers  to  the  capacity  to  perform  complex 
tasks in real-world environments and learn by experience: 

o  Machines  or  agents  that  are  capable  of  observing  their  environment, 
learning,  and  based  on  the  knowledge  and  experience  gained,  taking 
intelligent  action  or  proposing  decisions.  (EU  Commission,  2018,  p.19  – 
Annoni et al.) 

o  An AI system is a machine-based system that can, for a given set of human-
defined  objectives,  make  predictions,  recommendations  or  decisions 
influencing real or virtual environments. (OECD, 2019, p.23) 

o  Learning  systems,  that  is,  machines  that  can  become  better  at  a  task 
typically performed by humans with limited or no human intervention. (WIPO, 
2019, p.19) 

o 

functions 

learning,  understanding, 

o  AI is a term commonly used to describe machines performing human-like 
reasoning  and 

cognitive 
(e.g., 
interacting). (OECD, 2020, p.10 – Baruffaldi et al.) 
‘Artificial intelligence system’ (AI system) means software that is developed 
with one or more of the techniques and approaches listed in Annex I12 and 
can, for a given set of human-defined objectives, generate outputs such as 
content,  predictions,  recommendations,  or  decisions 
the 
influencing 
environments they interact with. (AI Act, European Commission 2021b) 
o  A system that is capable of acquiring, analyzing, and utilizing data to learn 
and make decisions based on that learning. (International Organization for 
Standardization (ISO), 2021) 

Taken together these definitions converge in their focus on machines or AI systems 
that (i) possess learning capabilities, (ii) can make intelligent decisions, (iii) influence 
the  environment,  (iv)  improve  tasks  autonomously,  and  (v)  exhibit  human-like 
cognitive functions. 

7.2  Data collection methodology 

The  data  used  in  this  work  are  taken  from  the  Web  of  Science  (WoS)  Core 
Collection. The analysis considers all scientific articles published in peer-reviewed 
journals  and  conference  proceedings,  and  some  residual  categories,  including 
books, series, etc.  

12 Annex I of the AI Act proposal 

31 

 
 
 
We employ a comprehensive list of 40+ search terms designed to encompass not 
only developments in AI technologies per se, but also AI-related developments and 
applications:  

Image  segmentation; 

Artificial intellig*; Automated reasoning; Backpropagation; Computer vision; Data 
mining;  Data  science;  Deep  learning;  Expert  system*;  Face  detection;  Feature 
extraction; Generative adversarial net*; Gesture recognition; Image classification; 
Intelligent 
Image  recognition; 
machine*;  Kernel  machine*;  Knowledge  representation;  Machine  intelligence; 
Machine  learning;  Machine  translation;  Meta-learning;  Multilayer  perceptron*; 
Natural language processing; Neural net*; Object detection; Object identification; 
Object recognition; Pattern recognition; Pose estimation; Reinforcement learning; 
Semantic  search;  Semi-supervised 
learning;  Sentiment  analysis;  Speech 
recognition; Statistical learning; Supervised learning; Text classification; Transfer 
learning; Transformer net*; Unsupervised learning; Voice recognition.  

Information  retrieval; 

The query returned all 1,275,134 publications from 2000–2022 containing at least 
one search term in the title, abstract, or keywords. Our approach ensures a lower-
bound estimate of AI-related scientific activity. 

32 

 
 
 
  
Table 3. AI-related keywords sorted by relevance  

We decompose the scientific literature into subdisciplines, aggregating 251 subject 
categories from the Web of Science into 21 macro-categories. This classification is 
inspired  and  enriched  by  the  taxonomies  developed  in  previous  scientometric 
research  (see,  e.g.,  Leydesdorff  and Rafols,  2009;  Chen,  2017).  A  paper  can  be 
classified in multiple categories. A paper that falls into a category other than that of 
Computer Science is considered an application of AI within a scientific domain. All 
papers co-classified as Computer Science are also included as AI applications. The 
trends discussed in this report are robust to the use of a more restrictive criterion, 
that is, considering as AI applications only those papers that do not fall within the 
Computer Science category. Although any attempt at suggesting a clear disciplinary 
structure of science is open to criticism, we believe that our approach is sufficiently 
comprehensive to capture the dynamics of science of interest to us in the context of 
the diffusion of AI applications within different domains. 

33 

 
 
 
 
To assign a paper to a country, we apply a weighted fractional count (WFC) based 
on author affiliation: the total FC available per paper is 1, which is shared between 
authors  affiliated  to  different  countries,  assuming  that  each  author  contributed 
equally to the research – i.e., a paper with two authors from two different countries 
(A and B) will be counted as 0.5 for country A and 0.5 for country B.  The main trends 
discussed in this paper are robust to other aggregation strategies (e.g., aggregating 
certain residual categories and full vs. weighted counts, among others). 

34 

 
 
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This paper provides new insights into the role of 
Artificial Intelligence in scientific research across 
various domains of science. It is evident that AI as 
applied to science and research has been growing at  
a significant pace in recent years, with China leading 
the way, followed by the EU and the US. Indeed,  
the EU’s performance is found to be highly 
heterogeneous across its Member States. If current 
trends persist, the probability of future scientific 
discoveries being driven primarily by AI applications 
and tools is set to increase significantly. Failure to 
keep pace with the development and uptake of AI 
in science poses important challenges for the EU, 
including the need to address concerns about 
strategic autonomy, trends in scientific work and 
the labour market and, more broadly, productivity and 
growth. 

Studies and reports