PRISMA-LLM: An Empirical Reporting Framework for AI-Assisted Systematic Reviews
Large language models (LLMs) and AI-enabled software increasingly participate in systematic-review decisions, yet the information needed to audit these workflows is reported inconsistently. We analyze SciLitBench, a corpus of 888 review-automation papers with 14,726 annotations, to characterize changes in methods, review-stage use, evaluation and reported limitations. Automation has shifted toward LLM- and software-facing workflows, including stages that can alter the evidence base. Since 2023, 38.0% of software/product papers reported no evaluation, compared with 9.3% of LLM papers. Reporting coverage increased with LLM workflow complexity, yet 52% of positive-only LLM evaluations still ...
| Authors | Miguel Zabaleta, Baihan Lin |
|---|---|
| Primary category | cs.SE |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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