Convergence between Artificial Intelligence and Audiovisual Production:
A Bibliometric Analysis of its Evolution, Trends and Contributions (2000-2024)
DOI:
https://doi.org/10.62161/revvisual.v17.5811Keywords:
Artificial intelligence, Machine learning, Deep learning, Audiovisual production, Audiovisual, IA generativeAbstract
Over the past 25 years, the convergence between artificial intelligence (AI) and audiovisual production has driven disruptive advancements with interdisciplinary impact. This study presents a comprehensive bibliometric analysis using the Bibliometrix tool in R Studio to examine the evolution of research in this field. Emerging trends, interinstitutional collaboration networks, and the geographical distribution of knowledge production are identified. In addition, the most influential authors, institutions, and countries leading scientific development are highlighted, emphasizing the relevance of interdisciplinary cooperation. The findings reveal the transformation of the field and pinpoint areas with high potential for future research. This contribution aims to guide new lines of inquiry and support the formulation of scientific and technological policies.
Downloads
Global Statistics ℹ️
|
122
Views
|
37
Downloads
|
|
159
Total
|
|
References
Albores, E. L. (2022). Netflix: análisis comparativo del consumo de los usuarios antes y durante la pandemia. Anuario Electrónico de Estudios en Comunicación Social "Disertaciones", 1-20.
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 959-975.
Botha, J., & Pieterse, H. (2020). Fake News and Deepfakes: A Dangerous Threat for 21 st Century Information Security. 15th International conference on Cyber Warfare and Security: ICCWS, (págs. 57-66).
Caballero, J., Sora-Domenjó, C., & Codina, L. (2024). Implicaciones de la inteligencia artificial generativa en la narrativa y la estética cinematográfica : un estudio sobre las convocatorias OpenDocs y +RAIN Film Festival. Anàlisi: Quaderns de Comunicació i Cultura, 55-76.
Casas Arias, M., Priego Díaz, A., & Lara-Martínez, M. (2024). La Revolución en la Creación Visual: la Inteligencia Artificial Generativa. VISUAL Review. International Visual Culture Review / Revista Internacional de Cultura , 227-244.
Chen, Y., Wang, Y., Yu, T., & Pan, Y. (2024). The Effect of AI on Animation Production Efficiency: An Empirical Investigation Through the Network Data Envelopment Analysis. Electronics (Switzerland).
Dale, R. (2022). The voice synthesis business: 2022 update. Natural Language Engineering, 401-408.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 285-296.
Encinas, A. (2024). Inteligencia artificial generativa y fabulación de relatos audiovisuales. Madrid: Universidad Francisco de Vitoria.
Faranguillo, J. (2023). La inteligencia artificial generativa y su impacto en la creación de contenidos mediáticos. m231102a10. doi:http://dx.doi.org/10.17502/mrcs.v11i2.710
Franco Lazarte, E. G. (2025). Audiovisual Narrative in the Age of Artificial Intelligence: Advances, Trends and Challenges: A Systematic Review. Journal of Educational and Social Research, 1-15. doi:10.36941/jesr-2025-0001
Hinojosa Becerra, M., Marín Gutiérrez, I., & Maldonado Espinosa, M. (2024). Inteligencia Artificial y la producción audiovisual. Salamanca: Comunicación Social Ediciones y Publicaciones. doi:https://doi.org/10.52495/c6.emcs.23.ti12
Lozano, J. d., & Mejías-Climent, L. (2023). Beyond the black mirror effect: the impact of machine translation in the audiovisual translation environment. Linguistica Antverpiensia, New Series – Themes in Translation Studies, 1–19.
Lungu-Stan, V. C., & Mocanu, I. G. (2024). 3D Character Animation and Asset Generation Using Deep Learning. Applied Sciences, 7234.
Reyes Flores, L. G., & Mejía Rivera, K. A. (2024). INTELIGENCIA ARTIFICIAL EN LA INVESTIGACIÓN CUALITATIVA: ANÁLISIS BIBLIOMÉTRICO DE LA PRODUCCIÓN CIENTÍFICA INDIZADA EN SCOPUS. New Trends in Qualitative Research, e1116.
Vizoso, Á., Vaz-álvarez, M., & López-García, X. (2021). Fighting deepfakes: Media and internet giants’ converging and diverging strategies against hi-tech misinformation. Media and Communication, 291-300.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Authors retain copyright and transfer to the journal the right of first publication and publishing rights
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
Those authors who publish in this journal accept the following terms:
-
Authors retain copyright.
-
Authors transfer to the journal the right of first publication. The journal also owns the publishing rights.
-
All published contents are governed by an Attribution-NoDerivatives 4.0 International License.
Access the informative version and legal text of the license. By virtue of this, third parties are allowed to use what is published as long as they mention the authorship of the work and the first publication in this journal. If you transform the material, you may not distribute the modified work. -
Authors may make other independent and additional contractual arrangements for non-exclusive distribution of the version of the article published in this journal (e.g., inclusion in an institutional repository or publication in a book) as long as they clearly indicate that the work was first published in this journal.
- Authors are allowed and recommended to publish their work on the Internet (for example on institutional and personal websites), following the publication of, and referencing the journal, as this could lead to constructive exchanges and a more extensive and quick circulation of published works (see The Effect of Open Access).
