{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T11:59:11Z","timestamp":1765454351634,"version":"3.46.0"},"reference-count":63,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T00:00:00Z","timestamp":1765411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Publications"],"abstract":"<jats:p>Generative artificial intelligence (GenAI) is rapidly permeating research practices, yet knowledge about its use and topical profile remains fragmented across tools and disciplines. In this study, we present a cross-disciplinary map of GenAI research based on the Web of Science Core Collection (as of 4 November 2025) for the ten tool lines with the largest number of publications. We employed a transparent query protocol in the Title (TI) and Topic (TS) fields, using Boolean and proximity operators together with brand-specific exclusion lists. Thematic similarity was estimated with the Jaccard index for the Top\u201350, Top\u2013100, and Top\u2013200 sets. In parallel, we computed volume and citation metrics using Python and reconstructed a country-level co-authorship network. The corpus comprises 14,418 deduplicated publications. A strong concentration is evident around ChatGPT, which accounts for approximately 80.6% of the total. The year 2025 shows a marked increase in output across all lines. The Jaccard matrices reveal two stable clusters: general-purpose tools (ChatGPT, Gemini, Claude, Copilot) and open-source\/developer-led lines (LLaMA, Mistral, Qwen, DeepSeek). Perplexity serves as a bridge between the clusters, while Grok remains the most distinct. The co-authorship network exhibits a dual-core structure anchored in the United States and China. The study contributes to bibliometric research on GenAI by presenting a perspective that combines publication dynamics, citation structures, thematic profiles, and similarity matrices based on the Jaccard algorithm for different tool lines. In practice, it proposes a comparative framework that can help researchers and institutions match GenAI tools to disciplinary contexts and develop transparent, repeatable assessments of their use in scientific activities.<\/jats:p>","DOI":"10.3390\/publications13040067","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T11:17:27Z","timestamp":1765451847000},"page":"67","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multidisciplinary Bibliometric Analysis of Differences and Commonalities Between GenAI in Science"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8484-0741","authenticated-orcid":false,"given":"Kacper","family":"Sieci\u0144ski","sequence":"first","affiliation":[{"name":"Faculty of Economic Sciences, Institute of Management and Quality Sciences, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1707-0553","authenticated-orcid":false,"given":"Marian","family":"Oli\u0144ski","sequence":"additional","affiliation":[{"name":"Faculty of Economic Sciences, Institute of Management and Quality Sciences, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17705\/1jais.00859","article-title":"Knowledge management perspective of generative artificial intelligence","volume":"25","author":"Alavi","year":"2024","journal-title":"Journal of the Association for Information Systems"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1001\/jamainternmed.2023.1838","article-title":"Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum","volume":"183","author":"Ayers","year":"2023","journal-title":"JAMA Internal Medicine"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e2314021121","DOI":"10.1073\/pnas.2314021121","article-title":"Can generative AI improve social science?","volume":"121","author":"Bail","year":"2024","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s12525-023-00680-1","article-title":"Generative artificial intelligence","volume":"33","author":"Banh","year":"2023","journal-title":"Electronic Markets"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Barrington, N. 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