As anyone in a science field knows, research is critical to advancing knowledge, testing theories, and driving innovation. The authors of this paper, “Scientometric Analysis of Artificial Intelligence Applications in Smart City,” wanted to understand the impact of research in the field of artificial intelligence on the development of smart cities.
Artificial intelligence as a formal field of study dates back to the summer of 1956 with the Dartmouth Summer Research Project on Artificial Intelligence. The event brought together a small group of scientists from computer science, mathematics, and cognitive psychology to explore the potential of machines to simulate human intelligence.
The field advanced slowly during the 1960s and ‘70s, then picked up pace during the 1980s. When the commercial use of the internet accelerated in the late ‘90s and early 2000s, AI experienced a resurgence and began to outperform humans in specific tasks. By 2020, AI was an integral part of people’s daily lives.
As the pace of AI advancements accelerated, there was a correlating increase in the amount of research related to AI, begging the question, “How important has this research been to the integration of AI into society?”
The authors of this paper sought to answer this question using scientometric analysis. This is a quantitative approach used to study the characteristics of scientific research and its communication by analyzing patterns and trends in scholarly literature to understand the structure, dynamics and impact of research. They specifically focused on research related to AI and smart cities.
A search on the Web of Science (WoS) –a database that indexes scholarly papers of high impact and citation rates in the fields of science, social science, humanities, and the arts – for articles containing the key words “artificial intelligence” and “smart city” resulted in 1,284 articles published between 1975 and 2025.
The analysis of these articles revealed a number of key findings related to annual production trends, maps based on author-keyword co-occurrence networks, country citation relationships, and thematic maps and visualizations.
1. Annual Scientific Production
A drastic increase in published articles was noted between 2017 and 2024, which may be related to changes in smart city policies, growing interest in AI application research, or academic driving factors.
2. Mapping based on the co-occurrence network of authors and keywords.
Many articles shared a common theme of privacy and security as it relates to AI and smart cities. Data privacy, a critical issue in digital transformation, necessitates collaboration across data security, AI, and deep learning fields to mitigate risks inherent in centralized data processing.
Another commonality related to smart cities and sustainable development, with papers focused on how leveraging technologies such as AI, smart grids, and blockchain and integrating them with social governance frameworks can create urban environments that are both efficient and ethically responsible.
A third common topic identified is the integration of intelligent technologies with social innovation, particularly in IoT, smart transportation, and social governance. Keywords suggest that these technologies are driving social innovation and digital transformation. AI and machine learning offer new solutions for traffic management and logistics, while reinforcement learning improves social services and efficiency.
Mapping based on author-keyword co-occurrence network
3. Map of Collaborating Countries
As demand for smart city technologies grows, cross-border cooperation becomes essential for developing and advancing smart cities globally. The scientometric analysis revealed where and to what extent global collaboration in the area of smart cities is happening.
Key players such as China, the United States, Canada, and Germany lead in smart transportation, energy management, and infrastructure, driving innovation and policy support. China’s market demand and technological progress have also advanced international collaboration in urban infrastructure and smart grids. Additionally, several Asian countries, including India, along with emerging markets in Southeast Asia, are increasingly becoming key participants in smart city cooperation.
By sharing best practices and technologies, smart cities can leverage complementary strengths, create synergies, and support smarter, greener, and more efficient urbanization.
Country Collaboration Map
4. Thematic Map
The scientometric analysis helped to create a thematic map which reveals the interaction between technology, societal needs, and innovation trends, pointing to potential future technological directions. Topics are categorized into four quadrants: basic topics, driving topics, subtopics, and emerging or declining themes. Basic topics include design optimization, algorithms, IoT, prediction, networking, and smart cities. Driving topics include internet challenges and healthy aging, while subtopics are safety context adoption and damage detection.
Final Thoughts
Using a scientometric analysis, this paper examines the current state and trends of AI applications in smart cities, highlighting both their vast potential and the challenges they face. The findings suggest that with ongoing technological advancements, AI will play an increasingly significant role in smart city development. However, its progress still depends on interdisciplinary collaboration, technological breakthroughs, and policy support. In the future, AI is expected to drive urban development toward a more intelligent and sustainable direction, thereby enhancing citizens’ quality of life and advancing the modernization of urban governance systems.
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