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Moving towards acceleration with accountability: a conceptual framework for AI-assisted systematic reviews
Mohammad Jay1,2, Joanne Abi-Jaoude2,3, Sharon Elizabeth Straus3,4,5
1Department of Medicine, Division of Endocrinology, University of Toronto, Toronto, Ontario, Canada.
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.
