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Introduction <p>The exponential increase in systematic reviews (SRs), accelerated by LLM-based generative AI and non-LLM automation tools, risks redundancy, overlap, and research waste. However, there is limited empirical evidence on how SRs that disclose AI use apply and report these tools in practice, including the extent of transparency and validation.</p> Aim <p>To assess the methodological and reporting features of SRs that explicitly acknowledge LLM-based and non-LLM automation tools’ use in a dedicated statement, and to examine how these features relate to bibliometric characteristics of these SRs.</p> Methods <p>An exploratory, cross-sectional, meta-research study with individual SRs as the unit of analysis. A random sample was drawn from a purposively defined stratum, comprising only SRs with designated AI statements. Screening was conducted by a single researcher; data extraction was performed by one researcher and independently verified by four others. Descriptive analyses were supplemented by Wilcoxon rank-sum tests, Spearman’s <i>ρ</i>, and <i>χ</i>² tests.</p> Results <p>We included 188 SRs; 75% reported using LLMs, and in 92% of studies LLM-based and non-LLM automation tools were used for manuscript writing. Reviews with designated AI statements were predominantly published in Elsevier or Elsevier-supported journals (70.2%). Only 42% referenced a pre-registered protocol; the median time from protocol registration to first journal submission was 267 days. Reviews with more included studies were published in higher-impact journals (<i>ρ</i> = 0.34, <i>p</i> &lt; 0.0001), as were reviews led by authors affiliated with high-income countries (<i>W</i> = 1931.5, <i>p</i> &lt; 0.0001). Reviews with more authors were more likely to have a pre-registered protocol (<i>χ</i>² = 20.54, <i>p</i> &lt; 0.0001), and pre-registered reviews more often adhered to a reporting checklist (<i>χ</i>² = 8.93, <i>p</i> = 0.0027).</p> Conclusions <p>LLM-based and non-LLM automation tools were used predominantly for writing. Sharing of prompts and human-validation procedures was insufficient, and many reviews exhibited methodological and reporting weaknesses. Clearer guidance is needed to support transparent, rigorous use of LLM-based and non-LLM automation tools in SRs.</p>

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Bibliometric, methodological and reporting characteristics of systematic reviews with explicit AI disclosure statements: an exploratory meta-research study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月11日

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  • Anastasios Bastounis,
  • Lukasz Lagojda,
  • William Sheppard,
  • George Daly,
  • Edith Poku,
  • Andrew Booth

摘要

Introduction

The exponential increase in systematic reviews (SRs), accelerated by LLM-based generative AI and non-LLM automation tools, risks redundancy, overlap, and research waste. However, there is limited empirical evidence on how SRs that disclose AI use apply and report these tools in practice, including the extent of transparency and validation.

Aim

To assess the methodological and reporting features of SRs that explicitly acknowledge LLM-based and non-LLM automation tools’ use in a dedicated statement, and to examine how these features relate to bibliometric characteristics of these SRs.

Methods

An exploratory, cross-sectional, meta-research study with individual SRs as the unit of analysis. A random sample was drawn from a purposively defined stratum, comprising only SRs with designated AI statements. Screening was conducted by a single researcher; data extraction was performed by one researcher and independently verified by four others. Descriptive analyses were supplemented by Wilcoxon rank-sum tests, Spearman’s ρ, and χ² tests.

Results

We included 188 SRs; 75% reported using LLMs, and in 92% of studies LLM-based and non-LLM automation tools were used for manuscript writing. Reviews with designated AI statements were predominantly published in Elsevier or Elsevier-supported journals (70.2%). Only 42% referenced a pre-registered protocol; the median time from protocol registration to first journal submission was 267 days. Reviews with more included studies were published in higher-impact journals (ρ = 0.34, p < 0.0001), as were reviews led by authors affiliated with high-income countries (W = 1931.5, p < 0.0001). Reviews with more authors were more likely to have a pre-registered protocol (χ² = 20.54, p < 0.0001), and pre-registered reviews more often adhered to a reporting checklist (χ² = 8.93, p = 0.0027).

Conclusions

LLM-based and non-LLM automation tools were used predominantly for writing. Sharing of prompts and human-validation procedures was insufficient, and many reviews exhibited methodological and reporting weaknesses. Clearer guidance is needed to support transparent, rigorous use of LLM-based and non-LLM automation tools in SRs.