Generative AI in Science: Applications, Challenges, and Emerging Questions
It addresses the evolving role of GenAI in science for researchers and practitioners, but is incremental as a review of existing literature.
This paper reviews the impact of Generative AI on scientific practices through a qualitative analysis of 39 highly cited sources, finding rapid adoption but unclear long-term implications and governance issues.
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.