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

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This study defines artificial intelligence (AI) using bibliometrics to track its growth and multidisciplinary contributions. The developed AI search strategy aids researchers and policymakers in understanding AI

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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.