Human-AI Collaboration to Identify Literature for Evidence Synthesis

Abstract Systematic approaches to evidence synthesis can improve the rigour, transparency, and replicability of a traditional literature review. However, these systematic approaches are time and resource intensive. We evaluate the ability of OpenAI’s ChatGPT to undertake two initial stages of evidence syntheses (searching peer-reviewed literature and screening for relevance) and develop a novel collaborative framework to leverage the best of both human and AI intelligence. Using a scoping review of community-based fisheries management as a case study, we find that with substantial prompting, the AI can provide critical insight into the construction and content of a search string. Thereafter, we evaluate five strategies for synthesising AI output to screen articles based on predefined inclusion criteria. We find low omission rates (< 1%) of relevant literature by the AI are achievable, which is comparable to that of human screeners. These findings show that generalised AI tools can assist reviewers with evidence synthesis to accelerate the implementation and improve the reliability of a review.