Transparent reporting of AI in systematic literature reviews: development of the PRISMA-trAIce checklist

Abstract

Background: Systematic literature reviews (SLRs) 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, offer the potential to accelerate this process, their use introduces challenges to transparency and reproducibility. Reporting guidelines such as the PRISMA-AI (Preferred Reporting Items for Systematic Reviews and Meta-Analyses–Artifi-cial Intelligence Extension) primarily focus on AI as a subject of research, not as a tool in the review process itself. Objective: To address the gap in reporting standards, this study aimed to develop and propose a discipline-agnostic checklist extension to the PRISMA 2020 statement. The goal was to ensure transparent reporting when AI is used as a methodological tool in evidence synthesis, fostering trust in the next generation of SLRs. Methods: The proposed checklist, named PRISMA-trAIce (PRISMA–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 (eg, CONSORT-AI [Consolidated Standards of Reporting Trials–Arti-ficial Intelligence] and TRIPOD-AI [Transparent Reporting of a Multivariable Prediction Model of Individual Prognosis or Diagnosis–Artificial Intelligence]). Relevant items from these frameworks were extracted, analyzed, and thematically synthesized to form a modular checklist that integrated with the PRISMA 2020 structure. Results: 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 the entire structure of an SLR, 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. Conclusions: PRISMA-trAIce establishes an important framework to improve the transparency and methodological integrity of AI-assisted systematic reviews, enhancing the trust required for the responsible application of AI-assisted systematic reviews 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.

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