Artificial intelligence tools for automating assessments of reporting guideline adherence: a protocol for a systematic review

Background Complete reporting of health-related research is necessary for users to understand, appraise, and apply research results appropriately. Reporting guidelines have been developed to support complete reporting. However, assessments of reporting guideline adherence remain inconsistent, time-consuming, and difficult to scale. Artificial intelligence (AI) tools, such as traditional natural language processing models and large language models, might provide a potential solution. While numerous AI tools have been developed, no comprehensive synthesis has been undertaken to investigate what they assess, how they are implemented and perform, and their potential utility. Objective This systematic review aims to synthesise the characteristics and findings of studies evaluating AI tools developed to assist or automate assessments of reporting guideline adherence. Methods We will search MEDLINE, Embase, Scopus, Europe PMC, ACM Digital Library, IEEE Xplore, arXiv and Cochrane Colloquium Abstracts, with no restrictions on date, language, or publication type. We will include studies that evaluate AI tools to assess adherence of health-related papers to any reporting guidelines. Two authors will independently screen records, extract data and assess risk of bias. We will extract study characteristics, AI tool details, how reporting guidelines are operationalised for AI assessment, AI implementation details, comparison details, and evaluation outcomes including agreement metrics, classification performance metrics, and utility indicators. We will present and summarise results through structured tables and plots, stratified by reporting guideline and AI tool type. Discussion This systematic review will provide a comprehensive synthesis of AI tools developed to automate assessments of reporting guideline adherence. It will provide interest holders with insights into what AI tools have been used, their implementation approaches, which AI tool types perform well, and any improvements that can be made to AI tools automating assessments of reporting guideline adherence in the future.