Tendencias investigativas en el uso de técnicas de inteligencia artificial en la investigación científica
Palabras clave:
Investigación remota, ChatGPT, innovación, herramientas, PRISMA-2020
Resumen
La inteligencia artificial (IA) ha transformado la investigación científica en la última década. Su capacidad para procesar grandes volúmenes de datos ha revolucionado áreas como las ciencias naturales y sociales, permitiendo la identificación de patrones, la generación de predicciones, y la creación de nuevos modelos teóricos y metodológicos. Sin embargo, su implementación enfrenta desafíos significativos, como la ausencia de un enfoque sistemático y estandarizado. El objetivo de esta investigación es examinar las tendencias investigativas en el campo. La metodología abarca las bases de datos de Scopus y Web Of Science. Los resultados revelan un crecimiento polinomial cúbico con los años 2023 y 2022 como los más relevantes. Los referentes temáticos fueron los autores Liu y Zhang, las revistas Innovation con Cognition y los países Estados Unidos y China. En la evolución temática se pasó de investigar sobre Scientific study of language a ChatGPT y Remote research, las palabras emergentes y crecientes fueron Generative AI, Scientific Integrity y ChatGPT. Se sugiere profundizar en los conceptos clave para enfrentar los desafíos y aprovechar las oportunidades que ofrece la inteligencia artificial en la investigación científica. Las conclusiones proporcionan una visión completa del estado actual y sugieren áreas prometedoras para estudios futuros.
Citas
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Gu, Y., & Zhuang, Y. (2019). Research on evaluation of university scientific research team based on SOM neural network. 2019 Chinese Automation Congress (CAC), 3251–3256.
Guo, S., Zhou, J., Li, Z., Zheng, L., Wang, X., Cheng, S., & Li, K. (2024). End-to-end machine-learning for high-gravity ammonia stripping: Bridging the gap between scientific research and user-friendly applications. Water Research, 248, 120790. https://doi.org/10.1016/j.watres.2023.120790
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Jiang, Y., Shao, X., & Peng, Y. (2021). Research and construction of scientific research project sharing platform based on neural network. 2021 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), 430–433.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science (New York, N.Y.), 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
Kanakaris, N., Karacapilidis, N., Kournetas, G., & Lazanas, A. (2020). Combining machine learning and operations research methods to advance the project management practice. In Operations Research and Enterprise Systems: 8th International Conference, ICORES 2019, Prague, Czech Republic, February 19-21, 2019, Revised Selected Papers 8 (pp. 135-155). Springer International Publishing.
Karbasi, S., Asadi, Z., Mohaghegh, Z., Saeedi, F., Ferns, G. A., & Bahrami, A. (2023). The relationship between dietary patterns and insomnia in young women. Neuropsychopharmacology Reports, 43(2), 228–238. https://doi.org/10.1002/npr2.12336
Karbasi, Z., Gohari, S. H., & Sabahi, A. (2023). Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies. Health Science Reports, 6(5), e1244. https://doi.org/10.1002/hsr2.1244
Kliman-Silver, C., Siy, O., Awadalla, K., Lentz, A., Convertino, G., & Churchill, E. (2020). Adapting user experience research methods for AI-driven experiences. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems.
Langs, G. (2024). Artificial intelligence in medical imaging is a tool for clinical routine and scientific discovery. Seminars in Arthritis and Rheumatism, 64(152321), 152321. https://doi.org/10.1016/j.semarthrit.2023.152321
Lee, C., Jang, M., Noh, M., & Cho, B. (2021). Machine learning aided KREONET for advanced scientific research support. 2021 International Conference on Information and Communication Technology Convergence (ICTC), 1683–1685.
Lematta, G. J., Corral, C. C., Buchanan, V., Johnson, C. J., Mudigonda, A., Scholcover, F., Wong, M. E., Ezenyilimba, A., Baeriswyl, M., Kim, J., Holder, E., Chiou, E. K., & Cooke, N. J. (2022). Remote research methods for Human–AI–Robot Teaming. Human Factors and Ergonomics in Manufacturing, 32(1), 133–150. https://doi.org/10.1002/hfm.20929
Longo, L. (2019, September). Empowering qualitative research methods in education with artificial intelligence. In World Conference on Qualitative Research (pp. 1-21). Cham: Springer International Publishing.
Nhu, A. N., & Xie, Y. (2023). Towards inherently interpretable deep learning for accelerating scientific discoveries in climate science. Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, 1–2.
Osama, M., Afridi, S., & Maaz, M. (2023).
ChatGPT: Transcending language limitations in scientific research using artificial intelligence. Journal of the College of Physicians and Surgeons--Pakistan: JCPSP, 1198–1200. https://doi.org/10.29271/jcpsp.2023.10.1198
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. International Journal of Surgery (London, England), 88(105906), 105906. https://doi.org/10.1016/j.ijsu.2021.105906
Ramos, L., Márquez, R., & Rivas-Echeverría, F. (2023). AI’s next frontier: The rise of ChatGPT and its implications on society, industry, and scientific research La próxima frontera de la IA: El surgimiento de ChatGPT y sus implicaciones en la sociedad, la industria y la investigación científica. Revista Ciencia e Ingeniería, 44(2). http://erevistas.saber.ula.ve/index.php/cienciaeingenieria/article/view/18872
Rossi, S., Rossi, M., Mukkamala, R. R., Thatcher, J. B., & Dwivedi, Y. K. (2024). Augmenting research methods with foundation models and generative AI. International Journal of Information Management, 77(102749), 102749. https://doi.org/10.1016/j.ijinfomgt.2023.102749
Salah, M., Abdelfattah, F., Al Halbusi, H., & Mohammed, M. (2023). Beyond the “Death of Research”: Reimagining the Human-AI Collaboration in Scientific Research. Changing Societies & Personalities, 7(4), 31–46. https://doi.org/10.15826/csp.2023.7.4.250
Schmitz, B. (2023). Improving accessibility of scientific research by artificial intelligence—An example for lay abstract generation. Digital Health, 9. https://doi.org/10.1177/20552076231186245
Tarazi, A. (2024). Comparative analysis of the bibliographic data sources using PubMed, Scopus, Web of Science, and Lens: Comparative analysis of bibliographic data sources. High Yield Medical Reviews, 2(1). https://doi.org/10.59707/hymrunhw4628
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., … Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. Innovation (Cambridge (Mass.)), 2(4), 100179. https://doi.org/10.1016/j.xinn.2021.100179
Yao, T., Wang, J., Wan, M., Xin, Z., Wang, Y., Cao, R., Li, S., & Chi, X. (2022). VenusAI: An artificial intelligence platform for scientific discovery on supercomputers. Journal of Systems Architecture, 128(102550), 102550. https://doi.org/10.1016/j.sysarc.2022.102550
Zaman, W. S. W. K., Karman, S. B., Ramlan, E. I., Tukimin, S. N. B., & Ahmad, M. Y. B. (2021). Machine learning in stem cells research: Application for biosafety and bioefficacy assessment. IEEE access: practical innovations, open solutions, 9, 25926–25945. https://doi.org/10.1109/access.2021.3056553
Zangrossi, P., Martini, M., Guerrini, F., de Bonis, P., & Spena, G. (2024). Large language model, AI and scientific research: why ChatGPT is only the beginning. Journal of neurosurgical sciences, 68(2). https://doi.org/10.23736/s0390-5616.23.06171-4
Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23(100224), 100224. https://doi.org/10.1016/j.jii.2021.100224
Zhou, X., Tang, J., Daley, M., Ahmad, S., & Bai, Z. (2021). Now, I want to teach it for real!: Introducing machine learning as a scientific discovery tool for K-12 teachers. En Lecture Notes in Computer Science (pp. 486–499). Springer International Publishing.
Alnoor, A., Wah, K. K., & Hassan, A. (Eds.). (2022). Artificial neural networks and structural equation modeling: Marketing and consumer research applications. Singapore: Springer.
Borger, J. G., Ng, A. P., Anderton, H., Ashdown, G. W., Auld, M., Blewitt, M. E., ... & Naik, S. H. (2023). Artificial intelligence takes center stage: exploring the capabilities and implications of ChatGPT and other AI‐assisted technologies in scientific research and education. Immunology and Cell Biology, 101(10), 923-935. https://doi.org/10.1111/imcb.12689
Casiraghi, S., & van Dijk, N. (2024). Ethics reviews in the European Union. Implications for the governance of scientific research in times of data science and Artificial Intelligence. Law, Innovation and Technology, 1-22. https://doi.org/10.1080/17579961.2024.2313798
Cheng, H. W. (2023). Challenges and limitations of ChatGPT and artificial intelligence for scientific research: a perspective from organic materials. AI, 4(2), 401-405. https://doi.org/10.3390/ai4020021
de Vasconcellos, V. G. (2023). Artificial Intelligence and Co-Authorship of Scientific Manuscripts: Discussions about Using ChatGPT in Scientific Research and Writing. Rev. Brasileira de Direito Processual Penal, 9, 1047.
Dresher, B. E., & Hornstein, N. (1976). On some supposed contributions of artificial intelligence to the scientific study of language. Cognition, 4(4), 321-398.
Durieux, V., & Gevenois, P. A. (2010). Bibliometric indicators: Quality measurements of scientific publication. Radiology, 255(2), 342–351. https://doi.org/10.1148/radiol.09090626
Eager, B., Deegan, C., & Fiedler, T. (2024). Insights into the application of AI-augmented research methods for informing accounting practice: the development–through AI-of accountability-related prescriptions pertaining to seasonal work. Meditari Accountancy Research. https://doi.org/10.1108/MEDAR-08-2023-2116
Elbadawi, M., Li, H., Basit, A. W., & Gaisford, S. (2024). The role of artificial intelligence in generating original scientific research. International Journal of Pharmaceutics, 652, 123741. https://doi.org/10.1016/j.ijpharm.2023.123741
González-Esteban y Patrici Calvo, E. (2022). Ethically governing artificial intelligence in the field of scientific research and innovation. Heliyon, 8(2), e08946. https://doi.org/10.1016/j.heliyon.2022.e08946
Gu, Y., & Zhuang, Y. (2019). Research on evaluation of university scientific research team based on SOM neural network. 2019 Chinese Automation Congress (CAC), 3251–3256.
Guo, S., Zhou, J., Li, Z., Zheng, L., Wang, X., Cheng, S., & Li, K. (2024). End-to-end machine-learning for high-gravity ammonia stripping: Bridging the gap between scientific research and user-friendly applications. Water Research, 248, 120790. https://doi.org/10.1016/j.watres.2023.120790
Jaillant, L., & Aske, K. (2024). Are Users of Digital Archives Ready for the AI Era? Obstacles to the Application of Computational Research Methods and New Opportunities. ACM Journal on Computing and Cultural Heritage, 16(4), 1-16. https://doi.org/10.1145/3631125
Jiang, Y., Shao, X., & Peng, Y. (2021). Research and construction of scientific research project sharing platform based on neural network. 2021 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), 430–433.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science (New York, N.Y.), 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
Kanakaris, N., Karacapilidis, N., Kournetas, G., & Lazanas, A. (2020). Combining machine learning and operations research methods to advance the project management practice. In Operations Research and Enterprise Systems: 8th International Conference, ICORES 2019, Prague, Czech Republic, February 19-21, 2019, Revised Selected Papers 8 (pp. 135-155). Springer International Publishing.
Karbasi, S., Asadi, Z., Mohaghegh, Z., Saeedi, F., Ferns, G. A., & Bahrami, A. (2023). The relationship between dietary patterns and insomnia in young women. Neuropsychopharmacology Reports, 43(2), 228–238. https://doi.org/10.1002/npr2.12336
Karbasi, Z., Gohari, S. H., & Sabahi, A. (2023). Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies. Health Science Reports, 6(5), e1244. https://doi.org/10.1002/hsr2.1244
Kliman-Silver, C., Siy, O., Awadalla, K., Lentz, A., Convertino, G., & Churchill, E. (2020). Adapting user experience research methods for AI-driven experiences. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems.
Langs, G. (2024). Artificial intelligence in medical imaging is a tool for clinical routine and scientific discovery. Seminars in Arthritis and Rheumatism, 64(152321), 152321. https://doi.org/10.1016/j.semarthrit.2023.152321
Lee, C., Jang, M., Noh, M., & Cho, B. (2021). Machine learning aided KREONET for advanced scientific research support. 2021 International Conference on Information and Communication Technology Convergence (ICTC), 1683–1685.
Lematta, G. J., Corral, C. C., Buchanan, V., Johnson, C. J., Mudigonda, A., Scholcover, F., Wong, M. E., Ezenyilimba, A., Baeriswyl, M., Kim, J., Holder, E., Chiou, E. K., & Cooke, N. J. (2022). Remote research methods for Human–AI–Robot Teaming. Human Factors and Ergonomics in Manufacturing, 32(1), 133–150. https://doi.org/10.1002/hfm.20929
Longo, L. (2019, September). Empowering qualitative research methods in education with artificial intelligence. In World Conference on Qualitative Research (pp. 1-21). Cham: Springer International Publishing.
Nhu, A. N., & Xie, Y. (2023). Towards inherently interpretable deep learning for accelerating scientific discoveries in climate science. Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, 1–2.
Osama, M., Afridi, S., & Maaz, M. (2023).
ChatGPT: Transcending language limitations in scientific research using artificial intelligence. Journal of the College of Physicians and Surgeons--Pakistan: JCPSP, 1198–1200. https://doi.org/10.29271/jcpsp.2023.10.1198
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. International Journal of Surgery (London, England), 88(105906), 105906. https://doi.org/10.1016/j.ijsu.2021.105906
Ramos, L., Márquez, R., & Rivas-Echeverría, F. (2023). AI’s next frontier: The rise of ChatGPT and its implications on society, industry, and scientific research La próxima frontera de la IA: El surgimiento de ChatGPT y sus implicaciones en la sociedad, la industria y la investigación científica. Revista Ciencia e Ingeniería, 44(2). http://erevistas.saber.ula.ve/index.php/cienciaeingenieria/article/view/18872
Rossi, S., Rossi, M., Mukkamala, R. R., Thatcher, J. B., & Dwivedi, Y. K. (2024). Augmenting research methods with foundation models and generative AI. International Journal of Information Management, 77(102749), 102749. https://doi.org/10.1016/j.ijinfomgt.2023.102749
Salah, M., Abdelfattah, F., Al Halbusi, H., & Mohammed, M. (2023). Beyond the “Death of Research”: Reimagining the Human-AI Collaboration in Scientific Research. Changing Societies & Personalities, 7(4), 31–46. https://doi.org/10.15826/csp.2023.7.4.250
Schmitz, B. (2023). Improving accessibility of scientific research by artificial intelligence—An example for lay abstract generation. Digital Health, 9. https://doi.org/10.1177/20552076231186245
Tarazi, A. (2024). Comparative analysis of the bibliographic data sources using PubMed, Scopus, Web of Science, and Lens: Comparative analysis of bibliographic data sources. High Yield Medical Reviews, 2(1). https://doi.org/10.59707/hymrunhw4628
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., … Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. Innovation (Cambridge (Mass.)), 2(4), 100179. https://doi.org/10.1016/j.xinn.2021.100179
Yao, T., Wang, J., Wan, M., Xin, Z., Wang, Y., Cao, R., Li, S., & Chi, X. (2022). VenusAI: An artificial intelligence platform for scientific discovery on supercomputers. Journal of Systems Architecture, 128(102550), 102550. https://doi.org/10.1016/j.sysarc.2022.102550
Zaman, W. S. W. K., Karman, S. B., Ramlan, E. I., Tukimin, S. N. B., & Ahmad, M. Y. B. (2021). Machine learning in stem cells research: Application for biosafety and bioefficacy assessment. IEEE access: practical innovations, open solutions, 9, 25926–25945. https://doi.org/10.1109/access.2021.3056553
Zangrossi, P., Martini, M., Guerrini, F., de Bonis, P., & Spena, G. (2024). Large language model, AI and scientific research: why ChatGPT is only the beginning. Journal of neurosurgical sciences, 68(2). https://doi.org/10.23736/s0390-5616.23.06171-4
Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23(100224), 100224. https://doi.org/10.1016/j.jii.2021.100224
Zhou, X., Tang, J., Daley, M., Ahmad, S., & Bai, Z. (2021). Now, I want to teach it for real!: Introducing machine learning as a scientific discovery tool for K-12 teachers. En Lecture Notes in Computer Science (pp. 486–499). Springer International Publishing.
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2025-01-30
Cómo citar
Rodríguez Flores, E. A., Garcés Giraldo, L. F., Valencia, J., & Valencia-Arias, A. (2025). Tendencias investigativas en el uso de técnicas de inteligencia artificial en la investigación científica. Revista Venezolana De Gerencia, 30(109), 351-380. https://doi.org/10.52080/rvgluz.30.109.23
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