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

2025-07-11

AI Summary

This study addresses the growing tension between the rapid expansion of generative artificial intelligence (GenAI) in scientific practice and the lagging development of governance frameworks. Using Boolean queries on the OpenAlex database, we systematically identified and qualitatively coded 39 highly cited interdisciplinary publications. Our analysis maps GenAI’s usage patterns, ethical risks, and accountability challenges across research, scholarly writing, healthcare, and education. Results reveal deep integration of GenAI into diverse scientific workflows, yet a critical absence of robust governance—particularly concerning academic integrity, algorithmic transparency, and the delineation of human responsibility. The study exposes structural uncertainties underlying GenAI’s swift adoption and proposes priority governance themes and empirically grounded research pathways toward trustworthy scientific AI. By synthesizing cross-domain evidence and identifying actionable gaps, this work provides a theoretical foundation and methodological reference for policy formulation and interdisciplinary collaboration in AI governance.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Application Domains: Humanities & Computational Social Science
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

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.

Problem

• Explores GenAI applications in science and practice
• Examines challenges and governance of GenAI adoption
• Identifies future research questions for GenAI

Innovation

• Boolean search for GenAI literature
• Qualitative coding of 39 papers
• Categorizing GenAI applications in science