Abstract
This paper analyzed the growth and multidisciplinary nature of Artificial Intelligence research during the last 60 years. Web of Science coverage since 1960 was considered, and a descriptive research was performed. A top-down approach using Web of Science subject categories as a proxy to measure multidisciplinarity was developed. Bibliometric indicators based on the core of subject categories involving articles and citing articles related to this area were applied. The data analysis within a historical and epistemological perspective allowed to identify three main evolutionary stages: an emergence period (1960–1979), based on foundational literature from 1950s; a re-emergence and consolidation period (1980–2009), involving a “paradigmatic” phase of development and first industrial approach; and a period of re-configuration of the discipline as a technoscience (2010–2019), where an explosion of solutions for productive systems, wide collaboration networks and multidisciplinary research projects were observed. The multidisciplinary dynamics of the field was analyzed using a Thematic Dispersion Index. This indicator clearly described the transition from the consolidation stage to the re-configuration of the field, finding application in a wide diversity of scientific and technological domains. The results demonstrated that epistemic changes and qualitative leaps in Artificial Intelligence research have been associated to variations in multidisciplinarity patterns.
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Abbas, N. N., Ahmed, T., Shah, S. H. U., Omar, M., & Park, H. W. (2019). Investigating the applications of artificial intelligence in cyber security. Scientometrics, 121(2), 1189–1211. https://doi.org/10.1007/s11192-019-03222-9
Alejo-Machado, O. J., Fernández-Luna, J. M., & Huete, J. F. (2015). Bibliometric study of the scientific research on “Learning to Rank” between 2000 and 2013. Scientometrics, 102(2), 1669–1686. https://doi.org/10.1007/s11192-014-1467-4
Alvargonzález, D. (2011). Multidisciplinarity, interdisciplinarity, transdisciplinarity, and the sciences. International Studies in the Philosophy of Science, 25(4), 387–403. https://doi.org/10.1080/02698595.2011.623366
Arencibia-Jorge, R., García-García, L., Galban-Rodriguez, E., & Carrillo-Calvet, H. (2020). The multidisciplinary nature of COVID-19 research. Iberoamerican Journal of Science Measurement and Communication, 1(1), 003. https://doi.org/10.47909/ijsmc.13
Arencibia-Jorge, R., Vega-Almeida, R. L., & Carrillo-Calvet, H. (2021). A new thematic dispersion index to assess multidisciplinarity at different levels of aggregations. In W. Glanzel, S. Heeffer, P. S. Chi & R. Rousseau (Eds.), Proceedings of the 18th International Conference of Scientometrics and Informetrics ISSI’2021 (pp. 1439–1440). Leuven, Belgium: International Society of Scientometrics and Informetrics.
Bache, K., Newman, D., & Smyth, P. (2013, August). Text-based measures of document diversity. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 23–31).
Baier-Fuentes, H., Cascón-Katchadourian, J., Sánchez, Á. M., Herrera-Viedma, E., & Merigó, J. (2018). A bibliometric overview of the international journal of interactive multimedia and artificial intelligence. International Journal of Interactive Multimedia and Artificial Intelligence, 5(3), 9–16. https://doi.org/10.9781/ijimai.2018.12.003
Baker, T., & Smith, L. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Retrieved from Nesta Foundation website: Retrieved from https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf [October 21, 2020]
Bar-Ilan, J. (2010). Web of Science with the conference proceedings citation indexes: The case of computer science. Scientometrics, 83(3), 809–824. https://doi.org/10.1007/s11192-009-0145-4
Baum, S. D. (2020). Artificial interdisciplinarity: Artificial intelligence for research on complex societal problems. Philosophy & Technology. https://doi.org/10.1007/s13347-020-00416-5
Bhattacharya, S. (2019). Some salient aspects of machine learning research: A bibliometric analysis. Journal of Scientometric Res, 8(2s), s85–s92. https://doi.org/10.5530/jscires.8.2.26
Bobadilla, J., Gutiérrez, A., Patricio, M. Á., & Bojorque, R. X. (2019). Analysis of scientific production based on trending research topics. An Artificial Intelligence case study. Revista Española De Documentación Científica, 42(1), 1–16. https://doi.org/10.3989/redc.2019.1.1583
Bordons, M., Morillo, F., & Gómez, I. (2004). Analysis of cross-disciplinary research through bibliometric tools. In H. F. Moed, W. Glanzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research (pp. 437–456). Kluwer.
Bornmann, L., & Mutz, R. (2015). Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references. Journal of the Association for Information Science and Technology, 66(11), 2215–2222. https://doi.org/10.1002/asi.23329
Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. In K. Frankish & M. Ramsey (Eds.), The Cambridge handbook of artificial intelligence. Cambrigde University Press.
Carley, S., & Porter, A. L. (2012). A forward diversity index. Scientometrics, 90(2), 407–427. https://doi.org/10.1007/s11192-011-0528-1
Carusi, C., & Bianchi, G. (2020). A look at interdisciplinarity using bipartite scholar/journal networks. Scientometrics, 122(2), 867–894. https://doi.org/10.1007/s11192-019-03309-3
Channell, D. F. (2017). A history of technoscience: Erasing the boundaries between science and technology. Routledge.
Chen, C. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences of the United States of America, 101(suppl.), 5303–5310. https://doi.org/10.1073/pnas.0307513100
Chen, C. (2017). Science mapping: A systematic review of the literature. Journal of Data and Information Science, 2(2), 1–40. https://doi.org/10.1515/jdis-2017-0006
Chen, X., Chen, J., Cheng, G., & Gong, T. (2020). Topics and trends in artificial intelligence assisted human brain research. PLoS ONE, 15(4), e0231192. https://doi.org/10.1371/journal.pone.0231192
Chiarello, F., Steiner, M. T. A., Oliveira, E. B. D., Arce, J. E., & Ferreira, J. C. (2019). Artificial neural networks applied in forest biometrics and modeling: State of the art (January/2007 to July/2018). Cerne, 25(2), 140–155. https://doi.org/10.1590/01047760201925022626
Darko, A., Chan, A. P., Adabre, M. A., Edwards, D. J., Hosseini, M. R., & Ameyaw, E. E. (2020). Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Automation in Construction, 112, 103081. https://doi.org/10.1016/j.autcon.2020.103081
de Price, D. J. S. (1963). Little science, big science. Columbia University Press.
Devyatkin, D. A., Suvorov, R. E., & Tikhomirov, I. A. (2017). A method for the identification of competence centers based on the example of the artificial intelligence domain. Scientific and Technical Information Processing, 44(4), 253–260. https://doi.org/10.3103/S0147688217040086
Fiala, D., & Tutoky, G. (2017). Computer science papers in Web of Science: A bibliometric analysis. Publications, 5(4), 23. https://doi.org/10.3390/publications5040023
Fiala, D., & Willett, P. (2015). Computer science in Eastern Europe 1989–2014: A bibliometric study. Aslib Journal of Information Management, 67(5), 526–541. https://doi.org/10.1108/AJIM-02-2015-0027
Gao, J., Huang, X., & Zhang, L. (2019). Comparative analysis between international research hotspots and national-level policy keywords on artificial intelligence in China from 2009 to 2018. Sustainability, 11(23), 6574. https://doi.org/10.3390/su11236574
Garner, J., Porter, A. L., Leidolf, A., & Baker, M. (2018). Measuring and visualizing research collaboration and productivity. Journal of Data and Information Science, 3(1), 54–81. https://doi.org/10.2478/jdis-2018-0004
Garner, J., Porter, A. L., & Newman, N. C. (2014). Distance and velocity measures: Using citations to determine breadth and speed of research impact. Scientometrics, 100(3), 687–703. https://doi.org/10.1007/s11192-014-1316-5
Glanzel, W., & Schubert, A. (2003). A new classification scheme of science fields and subfields designed for scientometric evaluation purposes. Scientometrics, 56(3), 357–367. https://doi.org/10.1023/a:1022378804087
Gonsalves, T. (2019). The summers and winters of artificial intelligence. In M. Khosrow-Pour (Ed.), Advanced methodologies and technologies in artificial intelligence, computer simulation, and human-computer interaction (pp. 168–179). IGI Global.
Gupta, B. M., & Dhawan, S. M. (2018). Artificial intelligence research in India: A scientometric assessment of publications output during 2007–16. DESIDOC Journal of Library & Information Technology. https://doi.org/10.14429/djlit.38.6.12309
Hendler, J. (2008). Avoiding another AI winter. IEEE Intelligent Systems, 23(2), 2–4.
Hinojo-Lucena, F. J., Aznar-Díaz, I., Cáceres-Reche, M. P., & Romero-Rodríguez, J. M. (2019). Artificial intelligence in higher education: A bibliometric study on its impact in the scientific literature. Education Sciences, 9(1), 51. https://doi.org/10.3390/educsci9010051
Hjørland, B. (2002). Epistemology and the socio-cognitive perspective in information science. Journal of the American Society for Information Science and Technology, 53(4), 257–270. https://doi.org/10.1002/asi.10042
Hjørland, B., & Albrechtsen, H. (1995). Toward a new horizon in information-science—domain-analysis. Journal of the American Society for Information Science, 46(6), 400–425. https://doi.org/10.1002/(SICI)1097-4571(199507)46:6%3c400::AID-ASI2%3e3.0.CO;2-Y
Ivancheva, L. E. (2001). The non-Gaussian nature of bibliometric and scientometric distributions: A new approach to interpretation. Journal of the American Society for Information Science and Technology, 52(13), 1100–1105. https://doi.org/10.1002/asi.1176
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004
Katz, J.S., & Hicks, D. (1995, June). The classification of interdisciplinary journals: A new approach. Paper presented at the Proceedings of the Fifth International Conference of the International Society for Scientometrics and Informetrics, River Forest, IL, (pp. 105–115).
Kaul, V., Enslin, S., & Gross, S. A. (2020). History of artificial intelligence in medicine. Gastrointestinal Endoscopy, 92(4), 807–812. https://doi.org/10.1016/j.gie.2020.06.040
Klavans, R., & Boyack, K. W. (2011). Using global mapping to create more accurate document-level maps of research fields. Journal of the American Society for Information Science and Technology, 62(1), 1–18. https://doi.org/10.1002/asi.21444
Kuhn, T. S. (1971). La estructura de las revoluciones científicas. Fondo de Cultura Económica.
Kulakli, A., & Osmanaj, V. (2020). Global research on big data in relation with artificial intelligence (A bibliometric study: 2008–2019). International Journal of Online and Biomedical Engineering, 16(02), 31–46.
Lei, Y., & Liu, Z. (2019). The development of artificial intelligence: A bibliometric analysis, 2007–2016. Journal of Physics: Conference Series, 1168(2), 022027.
Leinster, T., & Cobbold, C. A. (2012). Measuring diversity: The importance of species similarity. Ecology, 93(3), 477–489. https://doi.org/10.1890/10-2402.1
Lewis, J. (2020). How transdisciplinary is design? An analysis using citation networks. Design Issues, 36(1), 30–44. https://doi.org/10.1162/desi_a_00573
Leydesdorff, L. (2007). Betweenness centrality as an indicator of the interdisciplinarity of scientific journals. Journal of the American Society for Information Science and Technology, 58(9), 1303–1319. https://doi.org/10.1002/asi.20614
Leydesdorff, L., & Bornmann, L. (2016). The operationalization of fields as WoS subject categories (WCs) in evaluative bibliometrics: The cases of library and information science and science & technology studies. Journal of the American Society for Information Science and Technology, 67(3), 707–714. https://doi.org/10.1002/asi.23408
Leydesdorff, L., Carley, S., & Rafols, I. (2013). Global maps of science based on the new Web-of-Science Categories. Scientometrics, 94(2), 589–593. https://doi.org/10.1007/s11192-012-0784-8
Leydesdorff, L., de Moya-Anegón, F., & Guerrero-Bote, V. P. (2015). Journal maps, interactive overlays, and the measurement of interdisciplinarity on the basis of scopus data (1996–2012). Journal of the Association for Information Science and Technology, 66(5), 1001–1016. https://doi.org/10.1002/asi.23243
Leydesdorff, L., & Rafols, I. (2009). A global map of science based on the ISI subject categories. Journal of the American Society for Information Science and Technology, 60(2), 348–362. https://doi.org/10.1002/asi.20967
Liu, J., Kong, X., Xia, F., Bai, X., Wang, L., Qing, Q., & Lee, I. (2018). Artificial intelligence in the 21st century. IEEE Access, 6, 34403–34421. https://doi.org/10.1109/ACCESS.2018.2819688
Moed, H. F., De Bruin, R. E., & Van Leeuwen, T. N. (1995). New bibliometric tools for the assessment of national research performance: Database description, overview of indicators and first applications. Scientometrics, 33(3), 381–422. https://doi.org/10.1007/bf02017338
Morillo, F., Bordons, M., & Gómez, I. (2001). An approach to interdisciplinarity through bibliometric indicators. Scientometrics, 51(1), 203–222. https://doi.org/10.1023/a:1010529114941
Morin, E., & Delgado Díaz, C. (2017). Reinventar la educación. Abrir caminos a la metamorfosis de la humanidad. Editorial UH.
Moschini, U., Fenialdi, E., Daraio, C., Ruocco, G., & Molinari, E. (2020). A comparison of three multidisciplinarity indices based on the diversity of Scopus subject areas of authors’ documents, their bibliography and their citing papers. Scientometrics, 125(2), 1145–1158. https://doi.org/10.1007/s11192-020-03481-x
Moya-Anegón, F., Vargas-Quesada, B., Herrero-Solana, V., Chinchilla-Rodriguez, Z., Corera-Alvarez, E., & Muñoz-Fernandez, F. (2004). A new technique for building maps of large scientific domains based on the cocitation of classes and categories. Scientometrics, 61(1), 129–145. https://doi.org/10.1023/B:SCIE.0000037368.31217.34
Mugabushaka, A. M., Kyriakou, A., & Papazoglou, T. (2016). Bibliometric indicators of interdisciplinarity: The potential of the Leinster-Cobbold diversity indices to study disciplinary diversity. Scientometrics, 107(2), 593–607. https://doi.org/10.1007/s11192-016-1865-x
Munim, Z. H., Dushenko, M., Jimenez, V. J., Shakil, M. H., & Imset, M. (2020). Big data and artificial intelligence in the maritime industry: A bibliometric review and future research directions. Maritime Policy & Management, 47(5), 577–597. https://doi.org/10.1080/03088839.2020.1788731
Myers, J. P., Jr., & Yamakoshi, K. (2020). The Japanese fifth generation computing project: A brief overview. Journal of Computing Sciences in Colleges, 36(2), 53–60. https://doi.org/10.5555/3447065.3447072
Niu, J., Tang, W., Xu, F., Zhou, X., & Song, Y. (2016). Global research on artificial intelligence from 1990–2014: Spatially-explicit bibliometric analysis. ISPRS International Journal of Geo-Information, 5(5), 66. https://doi.org/10.3390/ijgi5050066
Porter, A.L., Schoeneck, D.J., Solomon, G., Lakhani, H., & Dietz, J. (2013). Measuring and mapping interdisciplinarity: Research & evaluation on education in science & engineering (“REESE”) and STEM. In American Education Research Association Annual Meeting, April 27–May 1, San Francisco.
Porter, A. L., Cohen, A. S., Roessner, J. D., & Perreault, M. (2007). Measuring researcher interdisciplinarity. Scientometrics, 72(1), 117–147. https://doi.org/10.1007/s11192-007-1700-5
Porter, A., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81(3), 719–745. https://doi.org/10.1007/s11192-008-2197-2
Porter, A. L., Roessner, D. J., & Heberger, A. E. (2008). How interdisciplinary is a given body of research? Research Evaluation, 17(4), 273–282. https://doi.org/10.3152/095820208X364553
Pudovkin, A. I., & Garfield, E. (2002). Algorithmic procedure for finding semantically related journals. Journal of the American Society for Information Science and Technology, 53(13), 1113–1119. https://doi.org/10.1002/asi.10153
Qian, Y., Liu, Y., & Sheng, Q. Z. (2020). Understanding hierarchical structural evolution in a scientific discipline: A case study of artificial intelligence. Journal of Informetrics, 14(3), 101047. https://doi.org/10.1016/j.joi.2020.101047
Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics, 82(2), 263–287. https://doi.org/10.1007/s11192-009-0041-y
Rao, C. R. (1982). Diversity and dissimilarity coefficients: A unified approach. Theoretical Population Biology, 21(1), 24–43. https://doi.org/10.1016/0040-5809(82)90004-1
Rokach, L., & Mitra, P. (2013). Parsimonious citer-based measures: The artificial intelligence domain as a case study. Journal of the American Society for Information Science and Technology, 64(9), 1951–1959. https://doi.org/10.1002/asi.22887
Ruiz-Castillo, J., & Waltman, L. (2014). Field-normalized citation impact indicators using algorithmically constructed classification systems of science. Journal of Informetrics, 9(1), 102–117. https://doi.org/10.1016/j.joi.2014.11.010
Savaget, P., Chiarini, T., & Evans, S. (2019). Empowering political participation through artificial intelligence. Science and Public Policy, 46(3), 369–380. https://doi.org/10.1093/scipol/scy064
Schubert, A., Glanzel, W., & Braun, T. (1989). Scientometric datafiles. A comprehensive set of indicators on 2,649 journals and 96 countries in all major science fields and subfields 1981–1985. Scientometrics, 16(1), 3–478. https://doi.org/10.1007/bf02093234
Schwab, K. (2017). The fourth industrial revolution. Crown Bussiness.
Serenko, A. (2010). The development of an AI journal ranking based on the revealed preference approach. Journal of Informetrics, 4(4), 447–459. https://doi.org/10.1016/j.joi.2010.04.001
Serna A., Acevedo E., & Serna E. (2017). Principios de la Inteligencia Artificial en las Ciencias Computacionales. In Serna E. (Ed.). Desarrollo e Innovación en Ingeniería. 2da ed. Antioquia: Editorial Instituto Antioqueño de Investigación.
Shi, Y., & Li, X. (2019). A bibliometric study on intelligent techniques of bankruptcy prediction for corporate firms. Heliyon, 5(12), e02997. https://doi.org/10.1016/j.heliyon.2019.e02997
Shneider, A. M. (2009). Four stages of a scientific discipline; four types of scientist. Trends in Biochemical Sciences, 34(5), 217–223. https://doi.org/10.1016/j.tibs.2009.02.002
Shukla, A. K., Janmaijaya, M., Abraham, A., & Muhuri, P. K. (2019). Engineering applications of artificial intelligence: A bibliometric analysis of 30 years (1988–2018). Engineering Applications of Artificial Intelligence, 85, 517–532. https://doi.org/10.1016/j.engappai.2019.06.010
Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface, 4(15), 707–719. https://doi.org/10.1098/rsif.2007.0213
Tabah, A. N. (1999). Literature dynamics: Studies on growth, diffusion, and epidemics. Annual Review of Information Science and Technology, 34, 249–286.
Thomas, J., & Zaytseva, A. (2016). Mapping complexity/human knowledge as a complex adaptive system. Complexity, 21(2), 207–234. https://doi.org/10.1002/cplx.21799
Tolcheev, V. O. (2019). Research and analysis of the subject area of deep learning. Automatic Documentation and Mathematical Linguistics, 53(3), 103–113. https://doi.org/10.3103/S000510551903004X
Tran, B. X., Nghiem, S., Sahin, O., Vu, T. M., Ha, G. H., Vu, G. T., Pham, H. Q., Do, H. T., Latkin, C. A., Tam, W., & Ho, C. S. (2019). Modeling research topics for artificial intelligence applications in medicine: Latent Dirichlet allocation application study. Journal of Medical Internet Research, 21(11), e15511. https://doi.org/10.2196/15511
Tseng, C. Y., & Ting, P. H. (2013). Patent analysis for technology development of artificial intelligence: A country-level comparative study. Innovation, 15(4), 463–475. https://doi.org/10.5172/impp.2013.15.4.463
Van den Besselaar, P., & Leydesdorff, L. (1996). Mapping change in scientific specialties: A scientometric reconstruction of the development of artificial intelligence. Journal of the American Society for Information Science, 47(6), 415–436. https://doi.org/10.1002/(SICI)1097-4571(199606)47:6%3c415::AID-ASI3%3e3.0.CO;2-Y
Vega-Almeida, R. L. (2010). Ciencia de la información y paradigma social: Enfoque histórico, epistemológico y bibliométrico para un análisis de dominio. Universidad de Granada.
Villalba Gómez, J. A. (2016). Problemas bioéticos emergentes de la inteligencia artificial. Diversitas: Perspectivas En Psicología, 12(1), 137–147. https://doi.org/10.15332/s1794-9998.2016.0001.10
Wagner, C. S., Roessner, J. D., Bobb, K., Klein, J. T., Boyack, K. W., Keyton, J., et al. (2011). Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of Informetrics, 5(1), 14–26. https://doi.org/10.1016/j.joi.2010.06.004
Waltman, L., & van Eck, N. J. (2012). A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology, 63(12), 2378–2392. https://doi.org/10.1002/asi.22748
Waltman, L., van Eck, N. J., van Leeuwen, T. N., Visser, M. S., & Van Raan, A. F. J. (2011). Towards a new crown indicator: Some theoretical considerations. Journal of Informetrics, 5(1), 37–47. https://doi.org/10.1016/j.joi.2010.08.001
Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1–37. https://doi.org/10.2478/jagi-2019-0002
West, E., Mutasa, S., Zhu, Z., & Ha, R. (2019). Global trend in artificial intelligence-based publications in radiology from 2000 to 2018. American Journal of Roentgenology, 213(6), 1204–1206. https://doi.org/10.2214/AJR.19.21346
Yu, D., Xu, Z., & Fujita, H. (2019). Bibliometric analysis on the evolution of applied intelligence. Applied Intelligence, 49(2), 449–462. https://doi.org/10.1007/s10489-018-1278-z
Zhang, X., Wang, X., Zhao, H., de Pablos, P. O., Sun, Y., & Xiong, H. (2019). An effectiveness analysis of altmetrics indices for different levels of artificial intelligence publications. Scientometrics, 119(3), 1311–1344. https://doi.org/10.1007/s11192-019-03088-x
Zhang, Y., Chen, H., Lu, J., & Zhang, G. (2017). Detecting and predicting the topic change of Knowledge-based systems: A topic-based bibliometric analysis from 1991 to 2016. Knowledge-Based Systems, 133, 255–268. https://doi.org/10.1016/j.knosys.2017.07.011
Zhao, L., Dai, T., Qiao, Z., Sun, P., Hao, J., & Yang, Y. (2020). Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse. Process Safety and Environmental Protection, 133, 169–182. https://doi.org/10.1016/j.psep.2019.11.014
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This research was supported by the program “Scientometrics, Complexity, and Science of Science”, at the Complexity Science Center of the National Autonomous University of Mexico (UNAM). We would like to thank Dr. Javier García-García for reviewing an earlier version of this article.
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Arencibia-Jorge, R., Vega-Almeida, R.L., Jiménez-Andrade, J.L. et al. Evolutionary stages and multidisciplinary nature of artificial intelligence research. Scientometrics 127, 5139–5158 (2022). https://doi.org/10.1007/s11192-022-04477-5
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DOI: https://doi.org/10.1007/s11192-022-04477-5