Generative AI in Science: Applications, Challenges, and Emerging Questions

TL;DR

This paper reviews the rapid adoption of Generative AI in scientific practices, exploring its applications, benefits, challenges, and unresolved questions about its long-term implications and governance.

Contribution

It provides a qualitative review of key literature on GenAI's role in science, highlighting current applications and raising questions for future research.

Findings

GenAI is rapidly adopted in scientific research and practice.

Uncertainties remain about the long-term impact and governance of GenAI.

Applications span scientific writing, medical practice, and education.

Abstract

This paper examines the impact of Generative Artificial Intelligence (GenAI) on scientific practices, conducting a qualitative review of selected literature to explore its applications, benefits, and challenges. The review draws on the OpenAlex publication database, using a Boolean search approach to identify scientific literature related to GenAI (including large language models and ChatGPT). Thirty-nine highly cited papers and commentaries are reviewed and qualitatively coded. Results are categorized by GenAI applications in science, scientific writing, medical practice, and education and training. The analysis finds that while there is a rapid adoption of GenAI in science and science practice, its long-term implications remain unclear, with ongoing uncertainties about its use and governance. The study provides early insights into GenAI's growing role in science and identifies questions for future research in this evolving field.