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

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. Plain language summary Many laboratory papers are hard to reproduce because important details are missing. Reporting checklists exist but their use is tedious and inconsistent. We built “AI compliance checkers” that read a manuscript and flag whether key information has been reported, pointing to the exact text as evidence. One checker targets the draft Good In Vitro Reporting Standards (GIVReSt), one applies the ToxRTool/Klimisch approach to judge the reliability of toxicology studies, and the third assesses animal study reporting against the ARRIVE guidelines. When we compared AI outputs with expert human assessments on benchmark papers, the tools agreed with humans most of the time. The main risk was the AI giving authors “the benefit of the doubt” unless run in a strict mode. Used appropri­ately, these tools can help authors avoid omissions and help journals screen papers more consistently while still requiring human oversight and further validation before regulatory use.