Predictive ML architectures are displacing inference-oriented techniques in interpretability-first fields, first via deep learning (2015–2021) and then via external commercial models (post-2022).
Duede, et al., Oil & Water? Diffusion of AI Within and Across Scientific Fields
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2roles
background 1polarities
support 1representative citing papers
AI use in science has grown exponentially since 2015 but stays confined to computer science and statistics topics, shows higher retraction rates and citations, and follows distinct global adoption patterns.
citing papers explorer
-
From inference to prediction: how machine learning is reconfiguring science
Predictive ML architectures are displacing inference-oriented techniques in interpretability-first fields, first via deep learning (2015–2021) and then via external commercial models (post-2022).
-
When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge
AI use in science has grown exponentially since 2015 but stays confined to computer science and statistics topics, shows higher retraction rates and citations, and follows distinct global adoption patterns.