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

Cosby K, Yang D, Fineberg HV. Assessing diagnostic performance. NEJM Evid. 2024. https://doi.org/10.1056/EVIDra2300232.

Article  PubMed  Google Scholar 

Blaizot A, Veettil SK, Saidoung P, Moreno-Garcia CF, Wiratunga N, Aceves-Martins M, et al. Using artificial intelligence methods for systematic review in health sciences: a systematic review. Res Synth Methods. 2022;13:353–62.

Article  PubMed  Google Scholar 

Johnson EE, O’Keefe H, Sutton A, Marshall C. The systematic review toolbox: keeping up to date with tools to support evidence synthesis. Syst Rev. 2022. https://doi.org/10.1186/s13643-022-02122-z.

Article  PubMed  PubMed Central  Google Scholar 

Van Dijk SHB, Brusse-Keizer MGJ, Bucsán CC, Van Der Palen J, Doggen CJM, Lenferink A. Artificial intelligence in systematic reviews: promising when appropriately used. BMJ Open. 2023;13: e072254.

Article  PubMed  PubMed Central  Google Scholar 

Hamel C, Kelly SE, Thavorn K, Rice DB, Wells GA, Hutton B. An evaluation of DistillerSR’s machine learning-based prioritization tool for title/abstract screening: impact on reviewer-relevant outcomes. BMC Med Res Methodol. 2020. https://doi.org/10.1186/s12874-020-01129-1.

Article  PubMed  PubMed Central  Google Scholar 

Tsou AY, Treadwell JR, Erinoff E, Schoelles K. Machine learning for screening prioritization in systematic reviews: comparative performance of Abstrackr and EPPI-Reviewer. Syst Rev. 2020. https://doi.org/10.1186/s13643-020-01324-7.

Article  PubMed  PubMed Central  Google Scholar 

Howard BE, Phillips J, Tandon A, Maharana A, Elmore R, Mav D, et al. SWIFT-active screener: accelerated document screening through active learning and integrated recall estimation. Environ Int. 2020;138:105623.

Article  PubMed  PubMed Central  Google Scholar 

Gates A, Gates M, Sebastianski M, Guitard S, Elliott SA, Hartling L. The semi-automation of title and abstract screening: a retrospective exploration of ways to leverage Abstrackr’s relevance predictions in systematic and rapid reviews. BMC Med Res Methodol. 2020. https://doi.org/10.1186/s12874-020-01031-w.

Article  PubMed  PubMed Central  Google Scholar 

Ferdinands G, Schram R, de Bruin J, Bagheri A, Oberski DL, Tummers L, et al. Performance of active learning models for screening prioritization in systematic reviews: a simulation study into the average time to discover relevant records. Syst Rev. 2023. https://doi.org/10.1186/s13643-023-02257-7.

Article  PubMed  PubMed Central  Google Scholar 

Reddy SM, Patel S, Weyrich M, Fenton J, Viswanathan M. Comparison of a traditional systematic review approach with review-of-reviews and semi-automation as strategies to update the evidence. Syst Rev. 2020. https://doi.org/10.1186/s13643-020-01450-2.

Article  PubMed  PubMed Central  Google Scholar 

Gates A, Guitard S, Pillay J, Elliott SA, Dyson MP, Newton AS, et al. Performance and usability of machine learning for screening in systematic reviews: a comparative evaluation of three tools. Syst Rev. 2019. https://doi.org/10.1186/s13643-019-1222-2.

Article  PubMed  PubMed Central  Google Scholar 

Marshall IJ, Wallace BC. Toward systematic review automation: a practical guide to using machine learning tools in research synthesis. Syst Rev. 2019. https://doi.org/10.1186/s13643-019-1074-9.

Article  PubMed  PubMed Central  Google Scholar 

Hamel C, Hersi M, Kelly SE, Tricco AC, Straus S, Wells G, et al. Guidance for using artificial intelligence for title and abstract screening while conducting knowledge syntheses. BMC Med Res Methodol. 2021. https://doi.org/10.1186/s12874-021-01451-2.

Article  PubMed  PubMed Central  Google Scholar 

Boetje J, van de Schoot R. The SAFE procedure: a practical stopping heuristic for active learning-based screening in systematic reviews and meta-analyses. Syst Rev. 2024;13:81.

Article  PubMed  PubMed Central  Google Scholar 

Callaghan MW, Müller-Hansen F. Statistical stopping criteria for automated screening in systematic reviews. Syst Rev. 2020. https://doi.org/10.1186/s13643-020-01521-4.

Article  PubMed  PubMed Central  Google Scholar 

O’Connor AM, Tsafnat G, Thomas J, Glasziou P, Gilbert SB, Hutton B. A question of trust: can we build an evidence base to gain trust in systematic review automation technologies? Syst Rev. 2019. https://doi.org/10.1186/s13643-019-1062-0.

Article  PubMed  PubMed Central  Google Scholar 

Van Mossel S, De Feria CR, De Geus-Oei L-F, Vriens D, Koffijberg H, Saing S. A systematic literature review of modelling approaches to evaluate the cost effectiveness of PET/CT for therapy response monitoring in oncology. Pharmacoeconomics. 2025;43:133–51.

Article  PubMed  Google Scholar 

van de Schoot R, de Bruin J, Schram R, Zahedi P, de Boer J, Weijdema F, et al. An open source machine learning framework for efficient and transparent systematic reviews. Nat Mach Intell. 2021;3:125–33.

Article  Google Scholar 

Oude Wolcherink MJ, Pouwels XGLV, van Dijk SHB, Doggen CJM, Koffijberg H. Can artificial intelligence separate the wheat from the chaff in systematic reviews of health economic articles? Expert Rev Pharmacoecon Outcomes Res. 2023;23:1049–56.

Article  PubMed  Google Scholar 

Harrison H, Griffin SJ, Kuhn I, Usher-Smith JA. Software tools to support title and abstract screening for systematic reviews in healthcare: an evaluation. BMC Med Res Methodol. 2020. https://doi.org/10.1186/s12874-020-0897-3.

Article  PubMed  PubMed Central  Google Scholar 

Higgins JPT, Lasserson T, Thomas J, Flemyng E, Churchill R. Methodological expectations of Cochrane intervention reviews. London: Cochrane; 2023.

Google Scholar 

Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. PRISMA explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2020;2021:372.

Google Scholar 

Comments (0)

No login
gif