Artificial Intelligence Driven Bibliometric Insights: Pioneering Down Syndrome Research

Shabana Shafi; Aijaz Ahmad Reshi; Arif Shah; Reem Hamad Al-Mutairi; Gharam Naif Al-Mutairi; Ajaz Ahmad

doi:10.52756/ijerr.2024.v37spl.006

Shabana Shafi Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia
Aijaz Ahmad Reshi Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia https://orcid.org/0000-0002-0795-2835
Arif Shah Department of Management, OPJS University, Rajasthan, India
Reem Hamad Al-Mutairi Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia
Gharam Naif Al-Mutairi Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia
Ajaz Ahmad Departments of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia https://orcid.org/0000-0001-6295-8685

DOI: https://doi.org/10.52756/ijerr.2024.v37spl.006

Abstract

The present bibliometric analysis investigates the scholarly output from 2013 to 2022 to explore the use of artificial intelligence (AI) in Down syndrome research. The analysis demonstrates a significant and rapid growth in publications, starting from a minimal number of 17 articles in 2013 and reaching a peak of 2162 by 2022. This indicates a notable increase in interest and dedication to this topic. Upon analyzing national contributions, the United States stands up as a frontrunner in terms of research output, citations, and collaborations. This underscores its crucial role in influencing the discussion and influence of AI-driven Down syndrome research. The dynamics of collaboration, especially between the United States and countries like the United Kingdom, China, and Germany, illustrate a vast worldwide network that facilitates the exchange of information. The congruence of these findings with past research highlights the regularity in exponential growth tendencies, which can be attributed to technical discoveries and interdisciplinary cooperation. Moreover, the prevalence of dominant nations, the significance of renowned publications, and the sway of prolific authors underscore the firmly established connections between research productivity and influence within specific fields of study. The study's findings suggest that future developments in AI-driven Down syndrome research will focus on integrating AI more deeply, fostering interdisciplinary collaborations, and prioritizing ethical considerations. These trends align with the anticipated paths and ethical obligations in this field.

Keywords: Artificial intelligence; Bibliometric Analysis; Down syndrome

References

Alrefaei, A. F., Hawsawi, Y. M., Almaleki, D., Alafif, T., Alzahrani, F. A., & Bakhrebah, M. A. (2022). Genetic data sharing and artificial intelligence in the era of personalized medicine based on a cross-sectional analysis of the Saudi human genome program. Sci. Rep., 12(1), 1405. https://doi.org/10.1038/s41598-022-05296-7

Alvino, L., Pavone, L., Abhishta, A., & Robben, H. (2020). Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research. Front. Neurosci., 14, 577666. https://doi.org/10.3389/fnins.2020.577666

Antonarakis, S. E., Skotko, B. G., Rafii, M. S., Strydom, A., Pape, S. E., Bianchi, D. W., . . . Reeves, R. H. (2020). Down syndrome. Nat. Rev. Dis. Primers., 6(1), 9. https://doi.org/10.1038/s41572-019-0143-7

Baldo, F., Piovesan, A., Rakvin, M., Ramacieri, G., Locatelli, C., Lanfranchi, S., . . . Pelleri, M. C. (2023). Machine learning based analysis for intellectual disability in Down syndrome. Heliyon, 9(9), e19444. https://doi.org/10.1016/j.heliyon.2023.e19444

Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare, 2020, 25-60.

Bulawit, G., Palaoag, T., & Bulawit Jr., B. (2023). Android-based Corn Disease Automated Recognition Tool Using Convolutional Neural Network. Int. J. Exp. Res. Rev., 30, 236-246. https://doi.org/10.52756/ijerr.2023.v30.021

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

Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809-1831. https://doi.org/10.1007/s11192-015-1645-z

Feldman, K., Johnson, R. A., & Chawla, N. V. (2018). The State of Data in Healthcare: Path Towards Standardization. J. Healthc Inform. Res., 2(3), 248-271. https://doi.org/10.1007/s41666-018-0019-8

Fernandez-Batanero, J.M, Montenegro-Rueda, M., Fernandez-Cerero, J., & Garcia-Martinez, I. (2020). Impact of the information and communication technologies on the education of students with Down syndrome: a bibliometric study (2008-2018). European Journal of Education Research., 9(1), 78-89.

Gupta, C., Chandrashekar, P., Jin, T., He, C., Khullar, S., Chang, Q., & Wang, D. (2022). Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases. J. Neurodev. Disord., 14(1), 28. https://doi.org/10.1186/s11689-022-09438-w

Haloi, R., Chanda, D., Hazarika, J., & Barman, A. (2023). Statistical feature-based EEG signals classification using ANN and SVM classifiers for Parkinson’s disease detection. Int. J. Exp. Res. Rev., 31(Spl), 141-149. https://doi.org/10.52756/10.52756/ijerr.2023.v31spl.014

Jana, N., Barik, S., & Arora, N. (2009). Current use of medical eponyms--a need for global uniformity in scientific publications. BMC Med. Res. Methodo.l, 9, 18. https://doi.org/10.1186/1471-2288-9-18

Joshi, I., Mondal, A. K., & Navab, N. (2023). Chromosome Cluster Type Identification Using a Swin Transformer. Applied Sciences-Basel, 13(14).

Kalis, B., Collier, M., & Fu, R. (2018). 10 promising AI applications in health care. Harvard Business Review.

Kammer, P. V., Moro, J. S., Martins-Junior, P. A., Cardoso, M., Bolan, M., & Santana, C. M. (2022). The 100 most-cited papers in dentistry for individuals with neurodevelopmental disorders: Bibliometric profile of scientific research. Spec. Care Dentist., 42(4), 369-375. https://doi.org/10.1111/scd.12684

Kaur, P. (2023). Performance and Accuracy Enhancement During Skin Disease Detection in Deep Learning. Int. J. Exp. Res. Rev., 35, 96-108. https://doi.org/10.52756/ijerr.2023.v35spl.009

Kondo, Tabu, S., Diwani, S.A., Nyamawe, A.S., & Mjahidi, M. M. (2023). Exploring the status of artificial intelligence for healthcare research in Africa: a bibliometric and thematic analysis. AI and Ethics. https://doi.org/10.1007/s43681-023-00359-5

Koul, A. M., Ahmad, F., Bhat, A., Aein, Q. U., Ahmad, A., Reshi, A. A., & Kaul, R. U. (2023). Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis. Biomedicines, 11(12). https://doi.org/10.3390/biomedicines11123284

Kumari, J., Kumar, E., & Kumar, D. (2023). A Structured Analysis to Study the Role of Machine Learning and Deep Learning in The Healthcare Sector with Big Data Analytics. Arch. Comput. Methods Eng., 1-29. https://doi.org/10.1007/s11831-023-09915-y

Leghari, I., & Ali, S. (2023). Artificial Intelligence Techniques to improve cognitive traits of Down Syndrome Individuals: An Analysis. International Journal of Software Science and Computational Intelligence, 15, 1-11.

Lewis, Matt, & Mercer, Elizabeth. (2023). The AI Roadmap – charting our future in healthcare communications. Current Medical Research and Opinion, pp. 1-3. https://doi.org/10.1080/03007995.2023.2261737

Lombardi, M., & Vannuccini, S. (2022). Understanding emerging patterns and dynamics through the lenses of the cyber-physical universe. Patterns (N Y), 3(11), 100601. https://doi.org/10.1016/j.patter.2022.100601

Merigo, J. M., & Nunez, A. (2016). Influential journals in health research: a bibliometric study. Global Health, 12(1),46. https://doi.org/10.1186/s12992-016-0186-4

Murillo-Llorente, M.T., Pérez-Bermejo, M., Llacer-Heredia, V., Tomás-Aguirre, B., Valls-Arévalo, A., & Tomás-Aguirre, F. (2023). Is There an Adequate Therapeutic Approach to Thyroid Pathology in Patients with Down Syndrome? Diagnostics, 13(23).

Okubo, Y. (1997). Bibliometric Indicators and Analysis of Research Systems: Methods and Examples", OECD Science, Technology and Industry Working Papers, No. 1997/01, OECD Publishing, Paris. https://doi.org/10.1787/208277770603.

Paredes, N., Caicedo-Bravo, E. F., Bacca, B., & Olmedo, G. (2022). Emotion Recognition of Down Syndrome People Based on the Evaluation of Artificial Intelligence and Statistical Analysis Methods. Symmetry-Basel, 14(12).

Reddy, N. S., & Khanaa, V. (2023). Diagnosing and categorizing of pulmonary diseases using Deep learning conventional Neural network. Int. J. Exp. Res. Rev., 31(Spl Volume), 12-22. https://doi.org/10.52756/10.52756/ijerr.2023.v31spl.002

Reshi, A. A., Ashraf, I., Rustam, F., Shahzad, H. F., Mehmood, A., & Choi, G. S. (2021). Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms. Peer. J. Comput. Sc.i, 7, e547. https://doi.org/10.7717/peerj-cs.547

Reshi, A. A., Rustam, F., Mehmood, A., Alhossan, A., Alrabiah, Z., Ahmad, A., . . . Choi, G. S. (2021). An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification. Complexity, 2021.

Rivera-Bonet, CN. (2023). Waisman Science Writer. Artificial intelligence: A real tool for advancing research on intellectual and developmental disabilities and beyond - Waisman Center - UW-Madison. Posted on July 10, 2023.

Subramanian, M., Wojtusciszyn, A., Favre, L., Boughorbel, S., Shan, J., Letaief, K. B., . . . Chouchane, L. (2020). Precision medicine in the era of artificial intelligence: implications in chronic disease management. J. Transl. Med., 18(1), 472. https://doi.org/10.1186/s12967-020-02658-5

Torres-Nunes, L., da Costa-Borges, P. P., Paineiras-Domingos, L. L., Bachur, J. A., de Sa-Caputo, D. D., & Bernardo, M. (2023). Sleep Disorder Prevalence among Brazilian Children and Adolescents with Down Syndrome: An Observational Study. Applied Sciences-Basel, 13(6).

Wang, M., Liu, X., Lai, Y., Cao, W., Wu, Z., & Guo, X. (2022). Application of Neuroscience Tools in Building Construction - An Interdisciplinary Analysis. Front. Neurosci., 16, 895666. https://doi.org/10.3389/fnins.2022.895666

Windsperger, K., & Hoehl, S. (2021). Development of Down Syndrome Research Over the Last Decades-What Healthcare and Education Professionals Need to Know. Front Psychiatry, 12, 749046. https://doi.org/10.3389/fpsyt.2021.749046

Zhang, H. G., Jiang, Y. T., Dai, S. D., Li, L., Hu, X. N., & Liu, R. Z. (2021). Application of intelligent algorithms in Down syndrome screening during second trimester pregnancy. World J. Clin. Cases., 9(18), 4573-4584. https://doi.org/10.12998/wjcc.v9.i18.4573