u/dzscholar

Those superscript numbers and links next to the authors' names (like the ones pointing to #ORCID, #Google Scholar, and #Scopus in the photo) serve as digital identifiers and profile links for the researchers.

Academic journals use these links for several important reasons:

* Disambiguation: Many #researchers share the exact same name. A unique identifier (like an ORCID ID or Scopus Author ID) ensures that papers are correctly attributed to the right person, preventing confusion between authors with similar or identical names.

* Academic Profiles & Metrics: They allow readers, editors, and institutions to quickly click through to view the author's complete publication history, citation counts, and overall research impact.

* Transparency and Verification: They prove the identity and institutional affiliation of the authors, adding credibility and authenticity to the published research.

* Networking and Collaboration: Other researchers can easily find how to contact or follow the work of specific authors in their field.

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Abstract

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

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