Los puntos clave no están disponibles para este artículo en este momento.
Generative Artificial Intelligence (GenAI) tools are increasingly integrated into research and academic writing, offering opportunities to streamline workflows and increase productivity. However, these tools also introduce risks when used uncritically, unethically, or without transparency. In particular, the undisclosed use of GenAI, now widely documented, may compromise research integrity. The aim of this AMEE Guide is to provide researchers with practical guidance on when and how to disclose the use of GenAI in scholarly writing. Specifically, we propose a clear framework to promote ethical GenAI use and reporting practices in health professions education research. We start with an exploration of key aspects of responsible use of GenAI in publishing (e.g. authorship, verification and responsibility, plagiarism and bias, data privacy and confidentiality, journal requirements). We then address the importance of transparency about GenAI use in research production, both within research teams (internal disclosure) and to journals and readers (external disclosure). With respect to the latter, we highlight the need to be aware of journal-specific guidance and offer guiding principles for effective disclosure. Central to these principles is the call for scholars to provide a candid description of how GenAI was used, allowing readers to understand how the model shaped the research and writing processes. We also briefly consider the use and disclosure of GenAI in peer review. Given that, at the time of writing this Guide (November 2025), many questions remain regarding AI use and disclosure for publishing, we conclude with reflections on future developments and directions for research.
Cleland et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: