The hunt for the last relevant paper: blending the best of humans and AI

Abstract

Background: The exponential growth of research literature makes it increasingly difficult to identify all relevant studies for systematic reviews and meta-analyses. While traditional search methods are labour-intensive, modern AI-aided approaches have the potential to act as a powerful 'super-assistant' during both the searching and screening phases. Objective: This paper evaluates how a combined, open-source approach - merging traditional and AI-aided search and screening methods - can help identify all relevant literature up to the 'last relevant paper' for a systematic review on post-traumatic stress symptom (PTSS) trajectories after traumatic events. Method: We applied eight search strategies, including database searches, snowballing, full-text retrieval, and semantic search via OpenAlex. All records were screened using a combination of human reviewers, active learning, and large language models (LLMs) for quality control. Results: On top of replicating the original 6,701 search results, we identified an additional 3,822 records using AI-aided methods. The combination of AI tools and human screening led to 126 relevant studies, with each method uncovering papers the others missed. Notably, machine-aided techniques helped find studies with missing keywords, unusual phrasing, or limited indexing. Across all AI-assisted strategies, 10 additional studies were identified, and while the overall yield was modest, these papers were unique and relevant and would likely have been missed using traditional methods. Conclusions: Our findings demonstrate that even when returns are low, AI-aided approaches can meaningfully enhance coverage and offer a scalable path forward when combined with screening prioritisation. A transparent, hybrid workflow where AI serves as a 'super-assistant' can meaningfully extend the reach of systematic reviews and increase the quality of the findings, but is not ready to replace humans fully.