Custom GPTs to aid in compliance checking for reporting standards in academic publishing

Abstract

Reporting standards have proliferated across biomedicine, yet incomplete methods reporting remains routine – less because the community doubts the value of transparency, but rather because compliance checking is tedious, inconsistently enforced, and poorly integrated into everyday writing and review. As a sequel to the Good In Vitro Reporting Standards (GIVReSt) argument that better reporting is essential infrastructure, this article explores a pragmatic next step: translating standards from static checklists into interactive, always-on guidance. We describe the development of three specialized “compliance copilots” built as custom GPT-based assistants – one aligned with the emerging GIVReSt, one reflecting the established ToxRTool reliability framework, and one mapped to ARRIVE for animal studies. The tools are designed to point to specific text evidence, flag missing essential information, and provide actionable suggestions while the manuscript is being written. Early benchmarking against expert assessments suggests that this approach can approx­imate human judgments for many checklist items in a fraction of the time and with high consistency. We also highlight why “strict” versus “lenient” interpretations matter, and why these systems should be framed as decision-support, not decision-makers. The central claim is cultural, not technical: arti­ficial intelligence (AI) will matter most when it makes rigorous reporting the path of least resistance, turning standards into routine practice rather than aspirational add-ons.