AutoReporter: development of an artificial intelligence tool for automated assessment of research reporting guideline adherence

Abstract Objectives To develop AutoReporter, a large language model (LLM) system that automates evaluation of adherence to research reporting guidelines. Materials and Methods Eight prompt-engineering and retrieval strategies coupled with reasoning and general-purpose LLMs were benchmarked on the SPIRIT–CONSORT–TM corpus. The top-performing approach, AutoReporter, was validated on BenchReport, a novel benchmark dataset of expert-rated reporting guideline assessments from 10 systematic reviews. Results AutoReporter, a zero-shot, no-retrieval prompt coupled with the o3-mini reasoning LLM, demonstrated strong accuracy (CONSORT 90.09%; SPIRIT: 92.07%), substantial agreement with humans (CONSORT Cohen’s κ = 0.70, SPIRIT Cohen’s κ = 0.77), runtime (CONSORT: 617.26 s; SPIRIT: 544.51 s), and cost (CONSORT: 0.68 USD; SPIRIT: 0.65 USD). AutoReporter achieved a mean accuracy of 91.8% and substantial agreement (Cohen’s κ > 0.6) with expert ratings from the BenchReport benchmark. Discussion Structured prompting alone can match or exceed fine-tuned domain models while forgoing manually annotated corpora and computationally intensive training. Conclusion Large language models can feasibly automate reporting guideline adherence assessments for scalable quality control in scientific research reporting. AutoReporter is publicly accessible at https://autoreporter.streamlit.app.