Alessandro De Cassai, Burhan Dost, John Augoustides, Leonard Azamfirei, Zekeriyya Alanoğlu, L. M. T. D. A. Azi, José Andrés Calvache, Vladimir Cerny, Stefan De Hert, Abdelazeem Eldawlatly, Michaela K. Farber, Diogo Sobreira-Fernandes, Michael R. Fettiplace, Dario Galante, Rakesh Garg, Heidi V. Goldstein, Alfredo Abad-Gurumeta, Lalit Gupta, Hugh C. Hemmings, Christopher A. Jones, Marc C. Hochberg, Joel Katz, Hyun Kang, GK Talu, Durval C. Kraychete, Ruth Landau, Sangseok Lee Lee, Hillary D. Lum, Christina Lundgren, P. T. R. Makuloluwa, Paolo Martelletti, Tonya M. Palermo, Philip J. Peyton, Pierrick Poisbeau, James P. Rathmell, Antoine Roquilly, Stephan K.W. Schwarz, Madhuragauri Shevade, Paul A. Sloan, BobbieJean Sweitzer, Ary Serpa Neto, Philip F. Stahel, Zerrin Özköse Şatırlar, Alparslan Turan, Dennis C. Turk, Massimiliano Valeriani, Mads U. Werner, Paul Young, Igor Borisovich Zabolotskikh, Kai Zacharowski, Szymon Zdanowski
This article presents a Delphi consensus developed by a panel of editors-in-chief of anaesthesiology and pain medicine journals to guide the responsible use of large language models (LLMs) in academic publishing. LLMs offer potential benefits for scientific writing, including language editing, summarisation, translation, information organisation, and support for non-native English speakers, but their misuse raises concerns about accuracy, transparency, confidentiality, and research integrity. Through a three-round modified Delphi process involving 53 editors-in-chief or their delegates, 59 statements were generated and categorised into guidance for authors, editors, reviewers, and publishers with a particular attention to LLM disclosure practices and perceived risks. The consensus recognises that LLMs are useful tools in academic publishing for authors, reviewers, and editors. However, their use must be guided by ethics, legality, and principles of transparency and accountability. LLMs may assist with limited editorial and authorial tasks provided that their use is fully disclosed and all outputs are verified by humans. The consensus also emphasises the inappropriateness of using LLMs to generate original or ideative content, which should remain a strictly human responsibility. Moreover, LLMs must not generate data, references, conclusions, or entire manuscripts, nor be used for editorial decisions or peer-review reports. Editors expressed concerns about 'hallucinations', erosion of critical skills, confidentiality breaches, and the proliferation of low-quality LLM-generated manuscripts. The resulting guidance highlights transparency, human accountability, and careful verification as essential principles for integrating LLMs into scholarly workflows while preserving the integrity of scientific publishing.