Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021
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Abstract (arXiv)
Analysing historical patterns of artificial intelligence (AI) adoption can inform decisions about AI capability uplift, but research to date has provided a limited view of AI adoption across various fields of research. In this study we examine worldwide adoption of AI technology within 333 fields of research during 1960-2021. We do this by using bibliometric analysis with 137 million peer-reviewed publications captured in The Lens database. We define AI using a list of 214 phrases developed by expert working groups at the Organisation for Economic Cooperation and Development (OECD). We found that 3.1 million of the 137 million peer-reviewed research publications during the entire period were AI-related, with a surge in AI adoption across practically all research fields (physical science, natural science, life science, social science and the arts and humanities) in recent years. The diffusion of AI beyond computer science was early, rapid and widespread. In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to cover over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. We note AI has experienced boom-bust cycles historically: the AI "springs" and "winters". We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jun 2023.
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Citing papers per year: 2025: 5; 2026: 3.
8 papers.
- 2025Science Is Falling Behind the Frontier: Foundation Model Adoption Across Half a Million Paperscited by 5
- 2025A Complement to Neural Networks for Anisotropic Inelasticity at Finite Strainscited by 4
- 2025The Right to AIcited by 3
- 2025Evidence-Driven Decision Support for AI Model Selection in Research Software Engineeringcited by 1
- 2026PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluationcited by 1
- 2026The Shrinking Lifespan of LLMs in Sciencecited by 1
- 2025Adaptation and Fine-tuning with TabPFN for Travelling Salesman Problemcited by 0
- 2026When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surgecited by 0
- 2026When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge7 May 2026
- 2026The Shrinking Lifespan of LLMs in Science8 Apr 2026
- 2026PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluation16 Jan 2026
- 2025Evidence-Driven Decision Support for AI Model Selection in Research Software Engineering12 Dec 2025
- 2025Science Is Falling Behind the Frontier: Foundation Model Adoption Across Half a Million Papers21 Nov 2025
- 2025Adaptation and Fine-tuning with TabPFN for Travelling Salesman Problem8 Nov 2025
- 2025A Complement to Neural Networks for Anisotropic Inelasticity at Finite Strains5 Oct 2025
- 2025The Right to AI29 Jan 2025