Mapping the AI Life Sciences Landscape in Greece: A National Survey and Bibliometric Comparison with Global Trends

Abstract

ABSTRACT Artificial intelligence is increasingly used in Life Sciences, though the pace and direction of adoption varies widely across countries. To map the Greek landscape, we combined two complementary approaches, a data-driven analysis of 916,824 AI-related life-science papers harvested from OpenAlex and PubMed and a targeted short survey at a national level aimed towards researchers, engineers, and support staff to be used as supporting material. We tagged each publication with Medical Subject Headings (MeSH) and compared topic frequencies between articles linked to at least one Greek institution and the rest of the world. Greek-affiliated outputs are disproportionately concentrated under the theme of methodology and algorithm-development, whereas the global corpus is dominated by disease-focused, organism-centered and clinical applications. Statistical contrasts across three MeSH hierarchy levels exposed clear national strengths in machine learning techniques and analytical tools, alongside under-representation in translational, patient-centred research. Survey responses reinforce these patterns: participants highlight limited access to well-curated biomedical data, constrained computational resources and difficulty recruiting cross-disciplinary talent as the chief barriers to progress. They advocate community-building actions (e.g., hackathons, postgraduate training, shared data infrastructures) that could realign national efforts with international practice while capitalizing on existing expertise in core-methodology development. Overall this study combines bibliometric evidence with community perspectives and provides a comprehensive overview of AI activity in Life Sciences in Greece, highlighting potential thematic strengths and gaps.

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