Artificial Intelligence Driven Bibliometric Insights: Pioneering Down Syndrome Research

Journal Title: International Journal of Experimental Research and Review - Year 2024, Vol 37, Issue 1

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.

Authors and Affiliations

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

Keywords

Related Articles

Assessment of the impact of the different point sources of pollutants on the river water quality and the evaluation of bioaccumulation of heavy metals into the fish ecosystem thereof

The present study focuses on the evaluation of the river (Ganga) water quality by estimating various quality indicators (also termed the physicochemical parameters) such as pH, temperature, dissolved oxygen (DO), electr...

Examining a generic streaming architecture for smart manufacturing's Big data processing in Anomaly detection: A review and a proposal

The smart manufacturing industry has witnessed a rapid increase in data generation due to the integration of sensors, IoT devices, and other advanced technologies. With this huge amount of data, the need for efficient da...

Popping balls papaya extract: Preparation of pediatric dosages in therapeutic formulations for therapeutic usage in dengue and malaria

Spherification is a cutting-edge molecular gastronomy method that has just emerged in the realm of food science and technology, and it may be used to produce foods with superior sensory qualities and a high level of cons...

Lack of community awareness on malaria and its vectors can impede malaria control: A case study in Great Nicobar Islands

Andaman and Nicobar Islands has historically been known for its high malaria transmission in the past. The aftermath of tsunami (2004), increased its risk and vulnerability, due to stagnant water bodies. Anopheles sundai...

Urban adult overweight and obesity prevalence in North Dum Dum, West Bengal, India

Obesity impacts most of the population, and many countries are predicted to raise the prevalence of adults affected by obesity (OB) and related disorders during the recent decades. OB is uninterruptedly increasing at a s...

Download PDF file
  • EP ID EP733350
  • DOI 10.52756/ijerr.2024.v37spl.006
  • Views 16
  • Downloads 0

How To Cite

Shabana Shafi, Aijaz Ahmad Reshi, Arif Shah, Reem Hamad Al-Mutairi, Gharam Naif Al-Mutairi, Ajaz Ahmad (2024). Artificial Intelligence Driven Bibliometric Insights: Pioneering Down Syndrome Research. International Journal of Experimental Research and Review, 37(1), -. https://europub.co.uk/articles/-A-733350