Eur J Psychotraumatol. 2025 Dec;16(1): 2546214
Rens van de Schoot,
Bruno Messina Coimbra,
Tale Evenhuis,
Peter Lombaers,
Felix Weijdema,
Laurens de Bruin,
Rutger Neeleman,
Elizabeth Grandfield,
Marit Sijbrandij,
Jelle Jasper Teijema,
Elena Jalsovec,
Michiel Pieter Bron,
Sonja Winter,
Jonathan de Bruin,
Mirjam van Zuiden.
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.
Keywords: Meta-analysis; Revisión sistemática; artificial intelligence; gran modelo de lenguaje; large language model; post-traumatic stress disorder; priorización del cribado; screening prioritisation; trastorno de estrés postraumático