The Machine Learning Divide: Geographic Asymmetry Revealed

Overview of the ML Global Impact Report 2025

Los Angeles, December 11, 2025 — Marktechpost has released the ML Global Impact Report 2025. This educational report's analysis includes over 5,000 articles from more than 125 countries, published within the Nature family of journals from January 1 to September 30, 2025. The scope is not a full assessment of global research but focuses on specific contributions.

Core Questions Addressed

The report focuses on three core questions:

1. In which disciplines has ML become part of the standard methodological toolkit, and where is adoption still sparse?
2. Which kinds of problems are most likely to rely on ML? Examples include high-dimensional imaging, sequence data, or complex physical simulations.
3. How do ML usage patterns differ by geography and research ecosystem?

Adoption in Applied Sciences

Machine Learning (ML) has frequently become integral within applied sciences and health research. It's often a crucial step in broader experimental workflows rather than the primary focus of research. The analysis indicates a concentration of ML adoption in these specific domains, enhancing existing research pipelines.

Common Use Cases for ML

The kinds of problems relying on ML typically involve complex data analysis tasks, including:

• Prediction
• Classification
• Segmentation
• Sequence modeling
• Feature extraction
• Simulation

This categorization underscores ML's utility across various research stages, from data processing to final output generation.

Geographic Asymmetry in ML Tool Usage

The report highlights a geographical divide between the origins of ML tools and usage patterns. The US produces most ML tools, whereas China contributes significantly to research, accounting for about 40% of ML-tagged papers, compared to the US's 18%. Key non-US tools include Scikit-learn (France), U-Net (Germany), and CatBoost (Russia).

Insights from the ML Global Impact Report

Overall, the ML Global Impact Report 2025 offers deep insights into the global research ecosystem. It confirms that ML is most commonly used in applied sciences and health research, aimed at tackling complex data challenges. A notable finding is the geographical split between the origin of ML tools and their heaviest users, particularly the high volume of research papers from China in relation to those from the US. These findings are specific to the analyzed corpus and highlight ongoing research workflows.