How Can Explainable Artificial Intelligence Accelerate the Systematic Literature Review Process?

Systematic literature reviews (SLRs) are key evidence requirements for health technology agency decision-making. However, the exponential increase in published articles makes a thorough and practical literature review increasingly challenging. To help researchers conduct an SLR, we developed a machine learning (ML)-based pipeline to accelerate the title and abstract screening (TIABS) step. We assessed this ML-based TIABS using various human-labeled SLRs to ensure its reproducibility.