An overview of artificial intelligence approaches for automating evidence synthesis

Artificial intelligence tools are increasingly being used to automate the evidence synthesis process, particularly for researcher-intensive tasks such as literature screening and data extraction. Researchers can face challenges in selecting appropriate tools from the large array available, often due to a limited understanding of their technicalities and therefore their capabilities and limitations. This paper provides a comprehensive overview of AI approaches leveraged by these evidence synthesis tools, examining the evolution from traditional machine learning to modern transformers such as large language models. We examine each approach's strengths and limitations within evidence synthesis, highlighting issues of accuracy, transparency, and task specificity. While AI has demonstrated significant potential for optimising researcher time and workload, important limitations remain regarding its statistical precision, interpretability, and reliability, which require careful consideration and continued human oversight. We conclude with recommendations for responsible adoption and future research directions to enhance the transparency and effectiveness of AI-assisted evidence synthesis.