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<p>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 performed a data‑driven analysis of 916,824 AI-related life-science papers harvested from OpenAlex and PubMed. 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. 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.</p>

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Mapping the AI life sciences landscape in Greece: a bibliometric comparison with global patterns

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

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  • Eleni Adamidi,
  • Serafeim Chatzopoulos,
  • Alexandros C. Dimopoulos,
  • Anastasia Krithara,
  • Anastasios Nentidis,
  • Nikos Pechlivanis,
  • Fotis Psomopoulos,
  • Thanasis Vergoulis,
  • Kleanthis Vichos

摘要

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 performed a data‑driven analysis of 916,824 AI-related life-science papers harvested from OpenAlex and PubMed. 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. 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.