Can artificial intelligence separate the wheat from the chaff in systematic reviews of health economic articles?

Abstract

OBJECTIVES: Artificial intelligence-powered tools, such as ASReview, could reduce the burden of title and abstract screening. This study aimed to assess the accuracy and efficiency of using ASReview in a health economic context. METHODS: A sample from a previous systematic literature review containing 4,994 articles was used. Previous manual screening resulted in 134 articles included for full-text screening (FT) and 50 for data extraction (DE). Here, accuracy and efficiency was evaluated by comparing the number of identified relevant articles with ASReview versus manual screening. Pre-defined stopping rules using sampling criteria and heuristic criteria were tested. Robustness of the AI-tool's performance was determined using 1,000 simulations. RESULTS: Considering included stopping rules, median accuracy for FT articles remained below 85%, but reached 100% for DE articles. To identify all relevant articles, a median of 89.9% of FT articles needed to be screened, compared to 7.7% for DE articles. Potential time savings between 49 and 59 hours could be achieved, depending on the stopping rule. CONCLUSIONS: In our case study, all DE articles were identified after screening 7.7% of the sample, allowing for substantial time savings. ASReview likely has the potential to substantially reduce screening time in systematic reviews of health economic articles.

Ähnliche Arbeiten