[Paper Review] Rise of Generative Artificial Intelligence in Science

Liangping Ding, Cornelia Lawson+1 more|Research Explorer (The University of Manchester)|Dec 30, 2024

Genetics, Bioinformatics, and Biomedical Research4 citations

TL;DR

This paper investigates the rise of generative artificial intelligence (GenAI) in scientific research using empirical analysis of OpenAlex data from 2017 to 2023. It reveals rapid growth in GenAI publications across disciplines, with the U.S. leading in output, and finds that GenAI research involves slightly smaller teams and sustained international collaboration despite geopolitical tensions.

ABSTRACT

Generative Artificial Intelligence (GenAI, generative AI) has rapidly become available as a tool in scientific research. To explore the use of generative AI in science, we conduct an empirical analysis using OpenAlex. Analyzing GenAI publications and other AI publications from 2017 to 2023, we profile growth patterns, the diffusion of GenAI publications across fields of study, and the geographical spread of scientific research on generative AI. We also investigate team size and international collaborations to explore whether GenAI, as an emerging scientific research area, shows different collaboration patterns compared to other AI technologies. The results indicate that generative AI has experienced rapid growth and increasing presence in scientific publications. The use of GenAI now extends beyond computer science to other scientific research domains. Over the study period, U.S. researchers contributed nearly two-fifths of global GenAI publications. The U.S. is followed by China, with several small and medium-sized advanced economies demonstrating relatively high levels of GenAI deployment in their research publications. Although scientific research overall is becoming increasingly specialized and collaborative, our results suggest that GenAI research groups tend to have slightly smaller team sizes than found in other AI fields. Furthermore, notwithstanding recent geopolitical tensions, GenAI research continues to exhibit levels of international collaboration comparable to other AI technologies.

Motivation & Objective

• To analyze the growth trajectory of generative AI (GenAI) publications in scientific research from 2017 to 2023.
• To examine the diffusion of GenAI research across scientific disciplines and geographical regions.
• To compare team size and international collaboration patterns in GenAI research versus other AI fields.
• To assess whether GenAI research exhibits distinct collaborative dynamics amid rising specialization and geopolitical tensions.

Proposed method

• Utilized OpenAlex, a comprehensive open academic database, to extract publication records for GenAI and broader AI research from 2017 to 2023.
• Classified publications based on field of study using OpenAlex’s subject classification system.
• Mapped geographical distribution of GenAI publications by country of affiliation to assess regional research contributions.
• Calculated team size as the number of authors per publication and compared mean team sizes across GenAI and non-GenAI AI research.
• Quantified international collaboration by computing the proportion of multi-country co-authored publications in GenAI and other AI fields.
• Applied time-series analysis to track growth patterns and diffusion trends over the study period.

Experimental results

Research questions

• RQ1 How has the volume of scientific publications on generative AI evolved from 2017 to 2023?
• RQ2 In which scientific disciplines has generative AI research most significantly diffused beyond computer science?
• RQ3 Which countries have contributed most significantly to global generative AI research output?
• RQ4 How do team sizes in generative AI research compare to those in other AI research domains?
• RQ5 To what extent does international collaboration in generative AI research mirror or differ from collaboration in other AI technologies?

Key findings

• Generative AI research experienced rapid growth, with publications increasing significantly across all scientific fields, not only in computer science.
• U.S. researchers contributed nearly 40% of all global GenAI publications, followed by China and several smaller advanced economies.
• GenAI research is increasingly present in non-computer science domains, indicating broad disciplinary adoption.
• GenAI research teams are slightly smaller on average than teams in other AI research fields, suggesting a trend toward more compact research units.
• Despite geopolitical tensions, international collaboration levels in GenAI research remain comparable to those in other AI technologies.
• The geographical spread of GenAI research shows strong contributions from medium-sized advanced economies, indicating broad global engagement beyond the U.S. and China.