Artificial Intelligence as a New Research Ally? Performing AI-Assisted Systematic Literature Reviews in Health Economics

PharmacoEconomics, vol. 43, pp. 647–650

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 Moreover, it may render an SLR outdated by the time it is published.

Authors 7

1. Sietse van Mossel Aachen Department of Radiology, Leiden University Medical Center

   Leiden University · University of Twente · Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Photonic Imaging, University of Twente, Enschede, The Netherlands

   Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands

2. Martijn J. Oude Wolcherink

   University of Twente

   Affiliation as printed

   Department of Health Technology and Services Research, University of Twente, Enschede, The Netherlands

3. Rafael Emilio de Feria Cardet

   University of Technology Sydney

   Affiliation as printed

   Centre for Health Economics Research and Evaluation, University of Technology Sydney, Sydney, NSW, Australia

4. Lioe‐Fee de Geus‐Oei Aachen Department of Radiology, Leiden University Medical Center

   Leiden University · University of Twente · Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Department of Biomedical Photonic Imaging, University of Twente, Enschede, The Netherlands

   Department of Radiation Science and Technology, Delft University of Technology, Delft, The Netherlands

   Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands

5. Dennis Vriens

   Radboud University Medical Center

   Affiliation as printed

   Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, The Netherlands

6. Hendrik Koffijberg

   University of Twente

   Affiliation as printed

   Department of Health Technology and Services Research, University of Twente, Enschede, The Netherlands

7. Sopany Saing corresponding

   University of Twente · University of Technology Sydney

   Affiliation as printed

   Centre for Health Economics Research and Evaluation, University of Technology Sydney, Sydney, NSW, Australia. s.saing@utwente.nl

   Department of Health Technology and Services Research, University of Twente, Enschede, The Netherlands. s.saing@utwente.nl