This section presents the bibliometric results extracted from the Core Collection of the Web of Science (WoS) of Clarivate Analytics, specifically using the Journal Citation Report as a basis, for the period 1990–2019 in the branch of knowledge related to AI.
Publication and citation trends in AI research
In the first row of Table 3, the indicator referring to the behavior of the Number of Articles is observed. The data are grouped into six five-year periods that also allow the analysis of three decades, in order to know the evolution of this field of knowledge (1990–1999; 2000–2009 and 2010–2019). The cumulative total numbers by five-year periods and decades indicate there is a sustained growth of the number of articles published in WoS in the field of AI in the period evaluated. When examining the cumulative data between the five-year period 1990–1994 and 2015–2019, a growth of more than 21 times was observed, which allows inferring that this branch of knowledge has grown steadily. The second row shows the Total Citation Score indicator and the behavior of the data confirms the interest in publishing more articles on AI and also the existence of an exponential increase in citations. In the third row, the indicator referring to the Total number of Authors can be seen and the data once again corroborate the constant expansion of this field of knowledge, presenting a growth of more than 46 times between the first and last five-year period. The three aforementioned numerical indicators grouped by five years and decades show the sustained progression of AI over the 30 years evaluated. By correlating these three indicators, it can be inferred that it is a subject of high scientific demand. In the fourth row, the Mean Citation Score indicator is observed and the data show a significant initial increase in the first three five-year periods for the mean number of citations, and then decrease with a less pronounced curve in the following two, until the last five-year period evaluated, where the cumulative number drops even to half of the first five-year period. This decline in recent years is attributed to the closeness regarding the time it takes to write and publish a paper and the time required to receive citations, and this behavior is a logical consequence of this scientific process. In the fifth row of the table is the indicator referring to Mean Authors per Article and the data added by five-year periods show the change in the dynamics of scientific publications referring, in this case, to the average number of co-authors participating in every published paper. The evidence shows a sustained increase in the average number of researchers and this behavior is relevant in this field of AI. The inference made regarding this indicator refers to the diversification that this branch of knowledge is having in applications to other fields of knowledge as well as to different application activities.
Figure 2 shows the behavior of the total number of articles published on AI in the WoS by year and five-year period. In the first decade, the annual data allow observing that in the years 1993 and 1996, there was a slight decrease in the total number of publications, but in general, in that period the production grew around four times when examining the extreme years (1990 and 1999). When studying the second decade, it can be seen that in 2001 and 2007, there was also a slight decrease in the production of scientific articles, but when examining the extreme years (2000 and 2009), it can be seen that there was a growth close to 2.4 times. When analyzing the last decade evaluated, the behavior of the number of publications changed significantly. In all the years, there was growth and the curve increased exponentially, indicating a growth of 8.1 times between the extreme years (2010 and 2019). These data corroborate the sustained progression of AI, mainly in the last five years of the period evaluated.
Figure 3 shows the evolutionary line of citations per year of AI in the WoS. This type of indicator fluctuates and this behavior is considered normal since it is not directly associated with the increase in publications but with the citations made in other published works in the same database. Nevertheless, this indicator is relevant to identify patterns of behavior on a research topic. In the case of AI, a fluctuating but upward increase is observed between the years 1990 and 2017. On the contrary, in the last two years studied, the number of citations decreased significantly, especially in 2019, and this is logical due to the scientific publication process itself. When analyzing the data, it can be seen that in 1990, the total number of citations was less than 5000. By the year 2000, the total number of citations already exceeded 60,000. In 2010, they exceeded 125,000 citations and for the year 2017, the year with the highest number of citations, they exceeded 190,000 citations. This growth pattern indicates that this branch of knowledge is increasingly important in the scientific field.
Table 4 shows the 10 most cited articles in the 3 decades studied, the number of authors and the base year of each of these publications. These indicators show that there are papers with a single author and others with 2 or more of them, indicating that there is no specific behavior in terms of the number of participants in highly cited papers; In this Top 10, It is also identified that there are articles published between 1995 and 2017, which allows inferring that the oldest ones could be seminal or marker papers in a specific topic and that continue to be current. The paper with the highest citation score was written by a single author and has more than twice as many citations as the second and this in turn was written by 2 authors and has more than 5800 citations with respect to the third study; from there, the distances are closer between the other seven articles with more citations received.
Top authors, journals, institutions, and countries in AI research
Table 5 presents the ten authors with the highest scientific production related to AI in the period studied. It also shows the relationship of each one of them with their appearance as first author (corresponding author) and the total number of citations, in addition to the indicators related to the most cited articles, for which the top decile has been taken into account. In this way, the aim is to make an evaluation that is less sensitive to the biases that a high number of citations can generate in specific articles. The first two authors exceeded 500 published articles and the three that follow have more than 400 publications in the period studied, indicating an average of more than 40 papers per year, which means that they are highly productive. The relationship between published articles and being the first author is higher for the second author (WANG, Y). When examining the total number of citations received per published article, the data show that the third-largest publisher (LIU, Y) received the most references in the period evaluated, followed by the first, second, and seventh authors. This evidence shows that the position of the authors varies when examining this aspect, which indicates that the measurement of the occurrence and absolute quantity as an indicator of the level of impact on the authors are relative values. For example, it can be noted that the author with the highest proportion of articles in the top decile of the most cited is the author who, in terms of the ranking by production, is in the 10th position in absolute terms. This indicates that it is essential to select the most appropriate variable to establish the authors’ positions.
Figure 4 shows the top 30 publishers of scientific articles related to AI in the WoS in the period evaluated. The analysis is performed for five-year periods. This figure shows that all the authors have published most of their papers in the last five years and more than 80% of them are in the range of the last 10 years, which allows inferring that it is an emerging and fast-growing branch of knowledge. Likewise, it is observed that all of them have published more than 200 articles in the period considered, 17 have less than 300 publications, eight have less than 400 publications, three have less than 500 publications, and two have more than that number. This means that these first 30 researchers are highly productive. To be more specific, it was found that there is a difference between the first two publishers of only eight articles in the entire period (544 and 536). The author with the most publications in the evaluated period was ZHANG L, managing to surpass the author WHAN Y in the last 5 years because the latter was the largest publisher in the period 2010–2014. Finally, this information allows observing the sustained growth of this branch of knowledge, but the data should be interpreted relatively since there is no direct relationship between greater quantity and the potential quality or visibility of each article published in this database.
In Table 6, the institutions with the highest number of affiliated authors are identified. This indicator is established by counting only once the assignment of an article to an organization regardless of the number of co-authors participating in it. In the Top 10, there are 6 institutions from the USA and 4 from China, indicating that these 2 countries are the global leaders in investment in science and technology in AI. The Chinese Academy of Sciences appears in the first place in the table with 2469 articles associated with it, almost a 1000 more publications than the Massachusetts Institute of Technology (MIT) of the USA, but it should be clarified that the former is a national organization of the People's Republic of China that brings together several research institutes nationwide. The differences between the remaining 8 institutions listed in the table do not show significant variations in the number of publications affiliated to each of them and they are also independent units. The total number of publications assigned in the study period to institutions in the USA was 7049 and to the People's Republic of China was 5470, which indicates the dominance of the world context in terms of the generation of new scientific knowledge in this topic.
Figure 5 shows the Top 10 organizations with the highest number of publications affiliated to them, which are dominated by 6 from the USA and 4 from the People's Republic of China. The importance of the figure lies in the appearance of the number of publications per five-year period and the relevance in terms of the total assigned per period. This allows longitudinal measurement of the initial efforts and the evolution of each organization. An important difference between the USA and China is that, except for Harvard, the other 5 US institutions were already publishing articles in AI in the first five-year period of the 1990s (1990–1994) and this institution appears in the second five-year period (1995–1999). The only Chinese institution that appears in the same five-year period is Nanyang Technological University; the other 3 appear in the first five years of this century. An indicator of the constant growth of this topic is that in the 10 organizations the largest number of published articles appears in the last five years (2015–2019), exceeding in all cases at least 40% of the total and in other cases more than 50%.
Table 7 shows the Top 10 of the countries with the highest production of articles on AI in the WoS in the period evaluated. This group of countries is geographically located in the northern hemisphere of the planet and makes up the so-called Triad that dominates the current world economy. The table shows that the leading country in the production of articles in this branch of knowledge is the USA, almost doubling that of China, which appears in the second position. A third group identifies the remaining eight countries in the Top 10, and in this case, all with less than half the number of articles published than those published by China, but in a relatively close relationship among them. A second indicator referred to as the Percentage of Total Articles Published, which is interpreted as a relative measure of frequency in which at least one author from a given country appears, shows the USA with 30.72% and China with 17.33%. Likewise, it can then be seen that the remaining eight countries in the Top 10 appear with similar frequencies, all less than 10%, which indicates that they are at a significant distance from the two leaders. Other evaluation indicators in the table refer to the Single Country Articles and Multiple Countries Articles that allow analyzing, for example, issues such as leadership in research, since the capacities of each country to generate endogenous knowledge in this field and the need for collaboration with other countries can be deducted. In this line, the USA collaborates relatively with 35.09%, China with 37.34%, while the countries that collaborate the most with others are England with 60.65%, France with 56.45%, and Germany with 55.60%. Another indicator that allows an aggregate assessment of the production of papers in the WoS, regarding the most cited articles, is the one referring to the upper decile, PP (top 10%), which, in this case, indicates that the USA has produced the highest proportion of articles in this decile and, on the contrary, China has the second-lowest proportion of articles in this segment. The growth observed in the number of articles from this group of countries cannot be interpreted as a competition but understood as a process in which funds are invested and more researchers are trained to generate new knowledge in this emerging field.
Figure 6 shows the Top 10 countries that have published the most articles on AI in the WoS in the 30 years studied. By having the data aggregated by five-year periods, the analysis can also be scaled to decades to understand the behavior of each country. First, it is observed that the Top 10 have published more articles in the last five years. In fact, China produced more than 70% of all its production in the last five years, almost equaling that of the USA. This marks a growth trend that suggests that, in the short term, China will lead the production of knowledge in this field. These two countries lead world production by a wide margin with respect to the other eight registered in the Top 10. It can be indicated that there are two groups, the first two, and the rest. One aspect that stands out in the figure, referring to the eight followers, is that they all show sustained growth every year. This group of eight countries makes up the second world force of intellectual production in AI.
Table 8 is composed of nine indicators that are contrasted with six five-year periods and its purpose is to show the longitudinal trajectory of each country; it is divided into five sections and each of them presents two countries from the Top 10. This first table shows the USA and China and in Appendix A, the other 4 tables with the other 8 countries with the most publications in AI in WoS are appended. When examining the number of publications per country, the USA appears as the largest producer in the overall total, but the growth trend indicates that China will be the leader in this field of knowledge in the next five years. By evaluating the total production of the ten countries examined, it was found that in the last five years, the growth of this group of countries was exponential. When studying the percentage of publications of each country in the last five years, taking this indicator as a relative measure, it was observed that the USA and China outstandingly lead the world; the rest of the 10 countries in the Top 10 make up a second group, quite homogeneous among themselves, which represents the other large percentage of the world. When examining the indicators that allow comparing articles from a single country and articles from several countries for the Top 10, it can be seen that, on the one hand, except for Korea, the other nine countries significantly increased their collaboration with third parties over the period. On the other hand, when looking at the relative percentage values in the last five-year period studied, Korea is the country that collaborated the least with other countries with 33.3% and England was the one that collaborated the most with 70.9%. With the analysis of the variable referring to the average number of citations per article in the last five-year period, it was found that the United States was once again number one with 13.23, but the second country was England with 13.05; in this indicator, China dropped to the sixth position with 10.64, leaving Japan in the last position of the Top 10 with 8.37. These indicators show development gaps between the Top 2 and the rest of the Top 10, but when comparing this group of countries leading the field of Artificial Intelligence with the rest of the world, the differences are insurmountable and generate a high dependence on this type of knowledge.
Figure 7 represents a network diagram that allows analyzing the relationships between the countries with the highest number of collaborations in terms of scientific production on AI in the WoS during the period studied. The density of each sphere is explained by the relative importance of each country and refers to the number of authors who publish with affiliation to institutions in each one of them. The figure shows three well-defined clusters, although these are not the only ones. The proximity between the spheres in each cluster establishes the number of co-authorships and is measured by the thickness of the lines connecting them. The country with the greatest density in the diagram is the USA, and it is the epicenter of the red cluster, which is made up of China, Canada, Japan, and South Korea, among others, and these five countries are part of the Top 10 examined in Table 8 and Appendix A. The second cluster in order of relevance is the green one and is composed of England, Italy, France, and Spain, among others, and these four countries are also part of the aforementioned Top 10. The third cluster in order of relevance in the diagram is blue and is led by Germany, the last of the Top 10 countries. This country appears in a central and equidistant position with respect to the other two clusters.
Major themes in AI research
Table 9 presents the Top 10 journals in which the most publications on the topic related to AI have appeared in the WoS in the 30 years studied. This table allows identifying the journals that belong to publishing platforms that offer fast publication services for a fee, which ensures an initial response in a maximum of six weeks, through the double-blind arbitration process. This Fast Review system that appeared in 2013 has changed the rules of the scientific game in terms of response time. Among them is IEEE ACCESS, which has the highest number of total articles, almost doubling the second one, which also belongs to a similar platform. The data in the table are affected by the percentage of total articles published on these new platforms, mainly in the last five years. On the other hand, when examining the correlation that allows observing the impact factor, by contrasting the indicator of the number of total citations in this Top 10 of Journals with the total number of published articles, IEEE drops from first to seventh place and the International Journal of Robotics Research placed in ninth place in the table, and created in 1982, moved to first place in this important line. This journal appears with 0.32 in the top decile indicator registered by the PP indicator (Top 10%), being significantly different from the other Top 9.
Figure 8 shows the first 30 journals measured by the number of articles published related to AI in the period evaluated. It can be seen that IEEE ACCESS occupies the first place, almost doubling in number the second. What is relevant about this data is that most papers were published in the last five years, which allows inferring that it is a fast-growing platform of high importance for researchers, even within the WoS database. This is significant because to climb rapidly up the quartile, it is necessary to receive many citations and meet all the Clarivate Analytics criteria. Of the 30 journals identified in the figure, there are seven that do not appear registered in the WoS before 2010 and this allows inferring that the ramification of new disciplines in the field of AI has produced an expansion in several science activities, one of them, new journals.
Table 10 presents 10 categories used by the WoS database as general descriptors that allow specialized searches in this database. The purpose of this exercise was to link these descriptors to AI in the period evaluated to find relationships with the Keywords and with the Journals, since they connect topics and disciplines. When observing the trajectories of each Category by five-year period, it can be seen that they have been changing over time. For example, Category number 1 in the final total (COMPUTER SCIENCE, AI) was the first in all five-year periods, except in the period 2015—2019. For its part, the second Category in the final total of publications (ENGINEERING, ELECTRICAL & ELECTRONIC) has been ranked second, third, or first in the different five-year periods evaluated. A revealing fact about the growth of topics related to AI is that the categories have grown in all the periods evaluated except for COMPUTER SCIENCE, INFORMATION SYSTEMS in the five-year period 1994–1999. When comparing the 10 categories taking into account the base five-year period (1990–1994) with the final five-year period (2015–2019), It is found that, in the least of cases, the growth was eight times greater and this confirms the constant expansion of the research activity around AI.
Directions researchers should pursue to advance AI research
Table 11 shows the Top 10 keywords linked to AI and this term is one more descriptor. The data in the table are grouped by five-year periods to segment the 30 years studied. This type of analysis of keywords is relevant because it allows determining the ramifications of a topic, the time of appearance, and the evolution of each descriptor. The table shows that in the first decade, AI was the term with the highest number of appearances, followed by Robotics and Machine Learning. In the second decade studied, Machine Learning moved to first place and Robotics to second place, displacing AI to third place. For the last decade examined, the patterns changed again, leaving Machine Learning as the number one descriptor, but Deep Learning quickly emerged to position itself as the second term, leaving AI in third place. The rest of the terms in the Top 10 grew steadily but at different rates, all of them being the core related to AI and considered as ramifications of new fields of knowledge.
Figure 9 allows studying not only the number of occurrences of the keywords but also the order of appearance of the terms related to AI and this analysis of co-words helps to identify conceptual structures and topics. In the figure, it is observed that Machine Learning is the most frequent term with 17,624 occurrences, doubling Deep Learning and tripling AI itself and Robotics as the two terms that follow it with the most occurrences. The difference between them is that Deep Learning is a new branch of knowledge that emerged abruptly in the last five years and it is placed as the one with the greatest growth potential. Another aspect that stands out in the figure is that most of the terms appear from 2010 onwards, but the growth curves increase substantially in the last five years, except for Genetic Algorithms, which maintains a similar average number of appearances in the six five-year periods examined.
From the network diagram shown in Fig. 10, it can be seen how each of the key terms that are part of the study are related. The proximity between the spheres establishes the number of co-occurrences, measured by the thickness of the lines connecting them. In Fig. 10, three clusters related to broad subjects are visible. The red cluster is more related to the development of theoretical knowledge, the green cluster is associated with more applied topics and the light blue cluster is more related to medical topics. The largest of the three clusters incorporates Machine Learning as the most relevant keyword, followed by Deep Learning and AI, all closely related. In the second cluster, Robotics is the keyword with the highest density, followed by Design and other topics such as Calibration, sensors, or Locomotion, demonstrating the level of technological applicability and the greater co-occurrence between them. The third cluster is represented by keywords such as Cancer, Surgery, Mortality, or Surgical Robotics, presenting specific characteristics of topics associated with developments linked to AI.
Figure 11 represents a term density map that allows performing another evaluation of the keywords most used by scientists in research associated with AI in the period evaluated. The density of each term shows the Ranking in which the importance of each one and its position regarding the centrality and density of the clusters represented are identified. The most relevant group of co-words is formed by Machine Learning, Deep Learning, and AI which, in addition, in Fig. 10, it can be seen that they are in the same cluster and this is measured by the co-occurrence between the terms. In order of importance, due to the density reflected in the map, Robotics and Design appear and belong to the second cluster and then, Cancer and Surgery are identified as the other most relevant terms and are in the third cluster. This term density map confirms the previous analysis of Figs. 9 and 10, with a different view of the results.