Leveraging ChatGPT-4 for Evidence Synthesis: A Case Study on the Use of a Large Language Model in a Systematic Review

Abstract

Artificial intelligence, particularly Large Language Models (LLM) such as ChatGPT, is emerging as a potentially transformative support for traditionally complex and time-consuming Systematic Literature Reviews (SLRs). In this study, we compared the traditional SLR process executed accordingly with Cochrane guidelines, with an AI-assisted approach using ChatGPT across various stages, from research question formulation to report writing. Effectiveness was assessed through quantitative measurements of time savings at each phase. Results showed substantial time reductions in several operational tasks, including Gantt chart, generating search terms and suggesting selection criteria. However, critical issues arose in stages requiring interpretative judgement, such as analyzing results, assessing risk of bias and final drafting. While AI cannot replace the role of the researcher, it is a valuable tool to optimize SLR workflow. The combination of human expertise and LLM capabilities presents a promising solution, provided it is accompanied by continuous development of AI systems to improve their reliability, transparency and interoperability.

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