ArticleGlobal epidemiology2026
Moving towards acceleration with accountability: a conceptual framework for AI-assisted systematic reviews.
Article in Global epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
7 authors.
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Abstract
Introduction: Artificial intelligence (AI) is being rapidly integrated into systematic review workflows, yet its impact on methodological rigor, transparency, and reporting quality remains poorly understood. This work examines the current use of AI assistance in systematic reviews and identifies gaps in existing appraisal frameworks. We aim to propose a conceptual methodological and illustrative framework that maps AI-assisted processes in the systematic review workflow. Methods: We conducted a conceptual methodological analysis informed by a targeted, non-systematic review of recent literature on AI-assisted systematic review workflows, mapped AI use across review stages, and evaluated alignment with existing appraisal and reporting frameworks (AMSTAR-2, PRISMA-2020, PRISMA-S, and ROBIS). Results: We identified a misalignment between AI-assisted systematic review workflows and existing methodological standards, which were developed for human-led systematic review workflows. We propose a conceptual framework that maps AI use across the systematic review process and delineates three core domains of methodological evaluation: transparency, reproducibility, and validity. Within this framework, we define key sources of methodological risk, such as prompt dependency, algorithmic reproducibility, and epistemic opacity, and illustrate how these risks may not be fully captured by current appraisal and reporting instruments such as AMSTAR-2, PRISMA, and ROBIS. Discussion: AI has the potential to support efficient systematic reviews, but credibility depends on transparent reporting, reproducible processes, and rigorous human verification. In our targeted evidence scan, empirical evaluations primarily addressed isolated AI-assisted tasks rather than complete systematic review workflows. Further methodological work is needed to evaluate whether, when, and under what conditions AI-assisted systematic reviews preserve the standards required for evidence-based decision-making; the proposed framework is intended to guide such work rather than serve as a validated appraisal instrument.
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