Artificial Intelligence as a New Research Ally?
Performing AI-Assisted Systematic Literature Reviews in Health Economics
Sietse van Mossel (Leiden University Medical Center, University of Twente)
Martijn Johan Oude-Wolcherink (University of Twente)
Rafael Emilio de Feria Cardet (University of Technology Sydney)
Lioe Fee de Geus-Oei (TU Delft - RST/Radiation, Science and Technology, Leiden University Medical Center, University of Twente)
Dennis Vriens (Radboud University Medical Center)
Hendrik Koffijberg (University of Twente)
Sopany Saing (University of Twente, University of Technology Sydney)
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Abstract
Systematic literature reviews (SLRs) are fundamental for aggregating published evidence, identifying knowledge gaps and informing health economic evaluations, especially in the field of novel diagnostics where randomised controlled trials are commonly absent [1]. The global publishing output within the field of novel diagnostics is increasing rapidly owing to increasing efforts towards precision medicine. Simultaneously, the global publishing output within the field of health economics is increasing rapidly owing to increasing efforts towards value-based healthcare and present budget constraints. The number of publications focussing on the health economic impact of diagnostics almost doubled from 86,244 in 2010 to 152,404 in 2025 (PubMed search using MeSH terms ‘Diagnostic Techniques and Procedures’ and ‘Health Care Economics and Organizations’). Consequently, the workload of performing SLRs is increasing as the number of articles that requires screening grows larger. The average time to complete an SLR is over 15 months, while the proportion of truly relevant articles for data extraction may be as low as 1% of the total search results [2]. The high workload may reduce researchers’ willingness to conduct an SLR or may lead to search strategies that are too narrow when prioritising time constraints over review quality. Moreover, it may render an SLR outdated by the time it is published.
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