Publications

Artificial intelligence (AI) is reshaping scientific practice. While recent advances in large language models (LLMs) are substantial, machine learning (ML) has a long history of developments across science. This study analyses the evolution of machine learning in global science from 1990 to 2025, by examining 4.9 million articles from OpenAlex. Using a hierarchical taxonomy of 252 ML techniques and architectures, we found that while deep learning exploded post-2015 and LLMs after 2022, established methods, such as linear and logistic regressions, declined in their share of the total scientific output. Utilizing state-of-the- art embeddings (mE5) and dimensionality reduction (UMAP), we constructed the ML Semantic Space that reveals a core-periphery structure dominated by the Physical Sciences. Computer Science acts as the primary methodological anchor, while Engineering drives applied developments. Health Sciences, led by Medicine, represents the second-largest expansion zone of ML techniques. 

Méndez Isla, M., Kozlowski, D. et Larivière, V. (2026). Assessing the use of machine learning techniques across scientific fields (1990–2025). Dans Conference Proceedings of the 30th Annual International Conference on Science and Technology Indicators (p. 1606-1617). Antwerp: ECOOM.