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

arXiv: https://arxiv.org/abs/2507.08310 · HTML: https://mlsanity.com/paper/2507.08310/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts](https://mlsanity.com/paper/2205.01833/) (2022)
- [Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers](https://mlsanity.com/paper/2311.10242/) (2023)
- [Rise of Generative Artificial Intelligence in Science](https://mlsanity.com/paper/2412.20960/) (2024)

## Built on by (most cited first, 1 of 1)

- [Structural shifts in institutional participation and collaboration within the AI arXiv preprint research ecosystem](https://mlsanity.com/paper/2602.03969/) (2026)

## Abstract (arXiv)

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.