AI-Assisted Systematic Literature Review of the Economic Burden of Pneumococcal Disease: Development and Validation Study.

BACKGROUND: Automated systematic literature review (SLR) may reduce the workload and errors associated with manual review, enabling faster, up-to-date reviews even with increasing publication volumes. Large language models (LLMs) have demonstrated strong capabilities in understanding unstructured languages. However, few studies have explored the potential of a comprehensive LLM platform to streamline the entire SLR process from article screening to data extraction. OBJECTIVE: This study aimed to investigate the feasibility of applying an LLM-based system to assist with SLR development. METHODS: We developed the Intelligent Systematic Literature Review (ISLaR 2.0) platform, powered by an LLM, and applied it to a use case of the economic burden of pneumococcal disease (PD) literature. First, we established the inclusion and exclusion criteria for the SLR. Second, we defined data elements related to economic burden and domain knowledge, along with guidelines for applying these definitions. Finally, we used the criteria and data element specifications to develop LLM prompts for screening and data extraction. For data extraction, we identified relevant study characteristics and economic burden outcomes. We evaluated ISLaR 2.0's performance against a gold standard of 50 expert-curated PD articles, using standard metrics (accuracy, precision, recall, and F1-score). We also conducted a qualitative analysis to describe errors made by the system. RESULTS: ISLaR 2.0 performed well in abstract and full-text screening (F1-scores of 86.27 for abstract screening and 87.18 for full-text screening) and data extraction from text (F1-scores of 92.83 for study details and 79.76 for economic burden outcomes). The F1-score for data extraction of tabular economic burden outcome data was 94.83. The qualitative analysis revealed 2 main challenges in extracting economic burden details: misclassification of cost categories and failure to extract relevant information. CONCLUSIONS: ISLaR 2.0 enabled efficient execution of an SLR regarding the economic burden of PD. The platform allowed users to flexibly define and modify criteria and data elements, supporting its use across a broad range of health research topics.