20.500.12556/DKUM-91189 Using generative artificial intelligence in bibliometric analysis 10 years of research trends from the European Resuscitation congresses Aims: The aim of this study is to use generative artificial intelligence to perform bibliometric analysis on abstracts published at European Resuscitation Council (ERC) annual scientific congress and define trends in ERC guidelines topics over the last decade. Methods: In this bibliometric analysis, the WebHarvy software (SysNucleus, India) was used to download data from the Resuscitation journal’s website through the technique of web scraping. Next, the Chat Generative Pre-trained Transformer 4 (ChatGPT-4) application programming interface (Open AI, USA) was used to implement the multinomial classification of abstract titles following the ERC 2021 guidelines topics. Results: From 2012 to 2022 a total of 2491 abstracts have been published at ERC congresses. Published abstracts ranged from 88 (in 2020) to 368 (in 2015). On average, the most common ERC guidelines topics were Adult basic life support (50.1%), followed by Adult advanced life support (41.5%), while Newborn resuscitation and support of transition of infants at birth (2.1%) was the least common topic. The findings also highlight that the Basic Life Support and Adult Advanced Life Support ERC guidelines topics have the strongest co-occurrence to all ERC guidelines topics, where the Newborn resuscitation and support of transition of infants at birth (2.1%; 52/2491) ERC guidelines topic has the weakest co-occurrence. Conclusion: This study demonstrates the capabilities of generative artificial intelligence in the bibliometric analysis of abstract titles using the example of resuscitation medicine research over the last decade at ERC conferences using large language models. generative artificial intelligence bibliometric analysis congress emergency medicine European Resuscitation Council generativna umetna inteligenca bibliometrična analiza kongres urgentna medicina Evropski svet za reanimacijo true true true ELSEVIER Angleški jezik Slovenski jezik https://www.sciencedirect.com/journal/resuscitation-plus © 2024 The Author(s). Published by Elsevier B.V. Znanstveno delo 2024-11-26 14:25:10 2024-11-27 10:20:55 2025-03-24 11:40:27 0000-00-00 00:00:00 2024 0 0 str. 1-5 Letn. 18, članek št. 100584 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-02-03 2024-02-08 2024-02-23 004.8:616-083.98 187063811 10.1016/j.resplu.2024.100584 2666-5204 3._Elsevier_clanek.pdf 3._Elsevier_clanek.pdf 1 7E5167370BB5F757856F299408F825AA c39ec5bdf780ce64e258ac33b90dc30232cc5c7dc2543a71341b32775d3cd575 e58ce98b-abf9-11ef-861f-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=186952 https://www.sciencedirect.com/science/article/pii/S2666520424000353?via%3Dihub 0 1e7fab14-089c-11f0-b559-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=189594 Fakulteta za zdravstvene vede Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0