Latvian researchers have completed 142 artificial intelligence projects and produced more than 1,900 scientific publications between 2018 and 2025, according to a new review from the Latvian Council of Science. The data shows AI shifting from a standalone research field into a practical tool that scientists across disciplines use to analyze data and find solutions.
Funding has flowed primarily through the Program for Fundamental and Applied Research, which backed 47 projects, while postdoctoral programs supported another 40. Young researchers lead nearly a third of all AI-related work, signaling strong momentum among the next generation of scientists. The University of Latvia, Riga Technical University, and the Institute of Electronics and Computer Science are the most active institutions, with 19 scientific bodies and several private and non-governmental organizations contributing to the effort.
Where the research happens
Machine learning remains the most sought-after technique. Latvian scientists apply it to assess soil conditions, analyze medical data, and develop new materials. Practical outputs include systems that automatically evaluate the biological state of soil, tools that improve diagnosis of chronic fatigue syndrome, and methods for creating more energy-efficient composite materials.
AI's footprint extends well beyond engineering and computer science. Projects span medicine, agriculture, linguistics, and social and humanitarian research. In most cases, the technology functions as a means to an end - a data-analysis engine - rather than the subject of study itself.
Rising publication volume and international reach
Scientific output on AI has grown sharply. In 2016, publications on artificial intelligence accounted for roughly 2.5% of all Latvian research papers. By 2025, that figure approached 9%. Almost half of these publications were produced through international collaboration, which the review's authors said leads to higher citation rates and greater global impact.
The review underscores a broader pattern: researchers who once studied AI as a discrete discipline now increasingly deploy it as a versatile instrument across fields. This shift mirrors trends seen in global science, where AI for Science & Research has moved from experimental labs into everyday workflows.
Why this matters for research professionals
The Latvian case illustrates a concrete funding and productivity model. With 47 fundamental and applied projects and 40 postdoctoral initiatives, the structure shows how targeted grants combined with early-career incentives can accelerate AI adoption across disciplines. For research managers and policy advisors, the takeaway is clear: channeling resources through both established programs and postdoctoral tracks creates a pipeline where AI serves as a practical Research tool, not a siloed specialty. The near-tripling of AI publication share over nine years also suggests that integrating these methods correlates with measurable gains in scientific output.
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