PRISMA-trAIce: A Proposed Checklist for Transparent Reporting of Artificial Intelligence in Comprehensive Evidence-Synthesis (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Systematic Literature Reviews (SLR) build the foundation for evidence synthesis, but they are exceptionally demanding in terms of time and resources. While recent advances in Artificial Intelligence (AI), particularly Large Language Models (LLMs), offer the potential to accelerate this process, their use introduces challenges to transparency and reproducibility. Developing reporting guidelines like PRISMA-AI primarily focus on AI as a subject of research, not as a tool in the review process itself. </sec> <sec> <title>OBJECTIVE</title> To address the gap in reporting standards, this paper aims to develop and propose a discipline-agnostic checklist extension to the PRISMA 2020 statement. The goal is to ensure transparent reporting when AI is used as a methodological tool in evidence synthesis, fostering trust in the next generation of SLRs. </sec> <sec> <title>METHODS</title> The proposed checklist, named PRISMA-trAIce (Transparent Reporting of Artificial Intelligence in Comprehensive Evidence-synthesis), was developed through a systematic process. We conducted a literature search to identify established, consensus-based AI reporting guidelines (e.g., CONSORT-AI, TRIPOD-AI). Relevant items from these frameworks were extracted, analyzed, and thematically synthesized to form a modular checklist that integrates with the PRISMA 2020 structure. </sec> <sec> <title>RESULTS</title> The primary result of this work is the PRISMA-trAIce checklist, a comprehensive set of reporting items designed to document the use of AI in SLRs. The checklist covers all phases of the review process, from title and abstract to methods and discussion, and includes specific items for identifying AI tools, describing human-AI interaction, reporting performance evaluation, and discussing limitations. </sec> <sec> <title>CONCLUSIONS</title> PRISMA-trAIce establishes an important framework to improve the transparency and methodological integrity of AI-assisted systematic reviews, enhancing the trust required for their responsible application in evidence synthesis. We present this work as a foundational proposal, explicitly inviting the scientific community to join an open science process of consensus-building. Through this collaborative refinement, we aim to evolve PRISMA-trAIce into a formally endorsed guideline, thereby ensuring the collective validation and scientific rigor of future AI-driven research. </sec> <sec> <title>CLINICALTRIAL</title> Not applicable. </sec>

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