Jul 2026· Journal of Korean medical science· Vol 41· 2 citations· 9 references
Medicine
TL;DR
It is essential to implement standardized frameworks that require explicit disclosure of AI use to ensure transparency and uphold trust in scholarly communication.
Abstract
The use of generative artificial intelligence (AI) in scholarly publishing is expanding rapidly, yet clear standards for its appropriate use and disclosure remain lacking. Surveys indicate that many researchers already use AI tools for manuscript preparation, particularly for writing assistance and error detection. However, attitudes toward acceptable AI use and disclosure requirements remain inconsistent, especially regarding the use of AI in drafting manuscripts and in the peer-review process. Analyses of manuscript submissions to major journal groups suggest that the proportion of authors disclosing AI use is substantially lower than estimates from researcher surveys, indicating possible underreporting or uncertainty about reporting requirements. It is therefore essential to implement standardized frameworks that require explicit disclosure of AI use to ensure transparency and uphold trust in scholarly communication.
Artificial intelligence (AI) in scholarly peer review has the potential to simplify the work of editors and reviewers through faster triage, automated reporting completeness checks, and scalable synthesis of reviewer comments. However, it also presents practical, ethical, and technical limitations that may place implem...
A. Babker· Frontiers in Artificial Inte...· 0 citations
The TITAN guidelines are reviewed as a practical framework for improving transparency and responsible reporting of AI use in medical research and scholarly publishing and can accommodate emerging AI technologies, including multimodal and agentic systems.
Arzoo Nazir, Shah Zeb· Electronic Journal of Medica...· 0 citations
Generative artificial intelligence (GenAI) is reshaping scholarly research faster than journals have developed stable norms for its use. This article presents an edited thematic account of a 2026 Joint Statistical Meetings panel that brought together editorial perspectives from mathematical statistics, data science, bi...
Jun Yan, Mark E. Glickman, Paul S. Albert et al.· 0 citations
The study provides empirical evidence supporting the development of a structured AI contribution taxonomy as a more principled and practical alternative to existing disclosure practices and suggests that effective AI disclosure governance should incorporate field-sensitive adaptation rather than relying on uniform impl...
Ayoung Yoon, Siena Oristaglio· Accountability in Research· 0 citations
An Integrated Risk-Based Framework for Responsible Generative AI Writing (IRF–GenAIW) is proposed that guides disclosure, verification, human oversight, authorship, confidentiality and accountability in Gen-AI use across various academic, professional and industrial sectors.
Richard Mulenga, M. Muhyila· American Journal of Multidis...· 0 citations