The extent to which Artificial Intelligence (AI) can trigger generalized paradigm shifts in science is unclear. Although some of these technologies have revolutionized data collection and analysis in specific scientific fields such as Chemistry, their overall impact depends on the scope of adoption and the ways scholars use them. In this study, we document substantial differences in the timing and extent of AI adoption across countries and scientific domains from 1960 to 2015. After 2015, we find generalized exponential growth in AI adoption, with the number of AI-supported works multiplying by at least four across all domains. The transformative nature of this rapid growth is less apparent and points to multiple challenges should adoption trends persist. According to our analyses, AI-supported research is confined to very few topics with strong ties to Computer Science and conventional statistical frameworks, suggesting limited transformational potential in epistemological terms. AI-supported works are also associated with an unwarranted citation premium and exhibit substantially higher retraction rates than non-AI-supported works across most fields. Geographically, AI adoption displays pronounced heterogeneity at the country level, along with an acceleration in the relevance of middle-income countries in Asia, from China and beyond. Thus, the transformative capacity of AI in science remains largely untapped, and its rapid adoption underlines challenges in research openness, transparency, reproducibility, and ethics from a global perspective. We discuss how best research practices could boost the benefits of AI adoption and highlight fields and geographies where these trends warrant closer scrutiny.

翻译：人工智能（AI）在多大程度上能引发科学领域的范式性变革尚不明确。尽管部分AI技术已在化学等特定科学领域革新了数据收集与分析方式，但其整体影响取决于应用范围及学者利用这些技术的方式。本研究系统记录了1960年至2015年间各国及科学领域在AI应用时间节点与程度上的显著差异。2015年之后，我们发现AI应用呈现普遍性指数增长态势，所有领域的AI辅助研究成果数量至少增长四倍。这种快速增长带来的变革性特征并不显著，若应用趋势持续，将引发多重挑战。我们的分析表明，AI辅助研究局限于与计算机科学和传统统计框架密切相关的极少数主题，这暗示其在认识论层面的变革潜力有限。AI辅助研究同时伴随不合理的引文优势，且多数领域中的撤稿率显著高于非AI辅助研究。在地理分布上，AI应用呈现显著的国家层面异质性，同时以中国为代表的亚洲中等收入国家在该领域的参与度加速提升。因此，AI在科学领域的变革潜力尚未充分释放，其快速应用正从全球视角凸显出研究开放性、透明度、可重复性与伦理方面的挑战。我们探讨了最佳研究实践如何增强AI应用的收益，并指出这些趋势需要密切关注的领域与地理区域。