Tracking developments in artificial intelligence research: constructing and applying a new search strategy

Area of Science:

• Bibliometrics
• Artificial Intelligence Research
• Scientific Development Tracking

Background:

• Artificial intelligence (AI) is a rapidly growing, multidisciplinary field requiring clear domain definition.
• Profiling AI's development and trajectories is crucial for researchers, managers, and policymakers.
• Existing methods for defining AI's scope may lack precision for comprehensive analysis.

Purpose of the Study:

• To propose a robust bibliometric definition for artificial intelligence (AI).
• To develop and validate a search strategy for identifying AI-related research.
• To analyze the growth, diffusion, funding, and disciplinary contributions of AI research.

Main Methods:

• Initiated with benchmark AI records using core keywords and specialized journals.
• Extracted and refined candidate terms from high-frequency keywords and AI subject categories.
• Assessed the proposed search strategy against three other methods using Web of Science data.

Main Results:

• The study successfully defined artificial intelligence (AI) using a bibliometric approach.
• The developed search strategy was validated against existing methods.
• Analysis revealed patterns in AI research growth, international diffusion, key funders, and interdisciplinary contributions.

Conclusions:

• The proposed bibliometric definition and search strategy effectively delineate the AI domain.
• This approach provides valuable insights for understanding AI's multidisciplinary landscape.
• Findings inform future research strategy development and identify areas for further investigation.