The extent to which Artificial Intelligence (AI) technologies can trigger generalized paradigm shifts in science is unclear. Although these technologies have revolutionized data collection and analysis in specific fields, their overall impact depends on the scope and ways of adoption. We analyze over 227 million scholarly works from the OpenAlex collection (1960-2024) spanning four scientific domains and 46 fields. To distinguish the use of AI as research method (AI adoption) from mentioning AI-related terms (AI engagement), we developed a two-step AI-assisted semantic classification pipeline, validated through human coding of 911 abstracts and a robustness check on 348,000 full-text articles (PLOS One). We document differences in the timing and extent of AI adoption across domains, with generalized exponential growth after 2015. The transformative nature of this growth, however, is less apparent. AI-supported research is confined to a few topics with strong ties to Computer Science and conventional statistical frameworks, suggesting limited epistemological transformation. It is also associated with an unwarranted citation premium and substantially higher retraction rates than non-AI-supported. Geographically, while wealthy countries lead in AI publications per capita, global South countries in a belt from Indonesia to Algeria lead in AI adoption relative to their national output, signaling a distinctive resource concentration pattern. The transformative capacity of AI in science thus remains untapped, and its rapid adoption underlines challenges in research openness, transparency, reproducibility, and ethics. We discuss how best research practices could boost the benefits of AI adoption and highlight areas that warrant closer scrutiny.

翻译：人工智能（AI）技术能否引发科学领域的普适性范式转变尚不明确。尽管这些技术已在特定领域革新了数据采集与分析方法，但其整体影响力取决于采用范围与方式。本研究基于OpenAlex数据库（1960-2024年）逾2.27亿篇学术成果，覆盖四大科学领域与46个学科。为区分"以AI作为研究方法"（AI采用）与"提及AI相关术语"（AI涉足），我们开发了双阶段AI辅助语义分类流程：通过911篇摘要的人工编码验证，并在34.8万篇全文文献（PLOS One期刊）中完成稳健性检验。研究发现各领域在AI采用的时间节点与程度存在差异，2015年后呈现全域性指数增长。然而，这种增长的变革性特征尚不明显：AI支撑的研究仍局限于与计算机科学及传统统计框架紧密相关的少数主题，暗示其认识论转型有限。同时，AI相关论文呈现异常引文溢价，撤稿率显著高于非AI支撑研究。地理维度上，虽然发达国家在人均AI论文产出方面领先，但从印度尼西亚延伸至阿尔及利亚的全球南方国家在AI采用量占本国总产出比例上表现突出，揭示了独特的资源集中模式。因此，AI在科学领域的变革潜力尚未充分释放，其快速普及更凸显了研究开放性、透明度、可重复性与伦理规范的挑战。本文探讨如何通过最佳研究实践提升AI采用效益，并指出需要重点关注的领域。