Perspective Open Access
Like 0


Many organisations struggle to keep pace with public health evidence due to the volume of published literature and length of time it takes to conduct literature reviews. New technologies that help automate parts of the evidence synthesis process can help conduct reviews more quickly and efficiently to better provide up-to-date evidence for public health decision making. To date, automated approaches have seldom been used in public health due to significant barriers to their adoption. In this Perspective, we reflect on the findings of a study exploring experiences of adopting automated technologies to conduct evidence reviews within the public health sector. The study, funded by the European Centre for Disease Prevention and Control, consisted of a literature review and qualitative data collection from public health organisations and researchers in the field. We specifically focus on outlining the challenges associated with the adoption of automated approaches and potential solutions and actions that can be taken to mitigate these. We explore these in relation to actions that can be taken by tool developers (e.g. improving tool performance and transparency), public health organisations (e.g. developing staff skills, encouraging collaboration) and funding bodies/the wider research system (e.g. researchers, funding bodies, academic publishers and scholarly journals).


Article metrics loading...

Loading full text...

Full text loading...



  1. Gough D, Davies P, Jamtvedt G, Langlois E, Littell J, Lotfi T, et al. Evidence Synthesis International (ESI): position statement. Syst Rev. 2020;9(1):155.  https://doi.org/10.1186/s13643-020-01415-5  PMID: 32650823 
  2. van Altena AJ, Spijker R, Olabarriaga SD. Usage of automation tools in systematic reviews. Res Synth Methods. 2019;10(1):72-82.  https://doi.org/10.1002/jrsm.1335  PMID: 30561081 
  3. Cochrane Library. About Cochrane Reviews. [Accessed: 27 Oct 2023]. Available from: https://www.cochranelibrary.com/about/about-cochrane-reviews
  4. Scott AM, Forbes C, Clark J, Carter M, Glasziou P, Munn Z. Systematic review automation tools improve efficiency but lack of knowledge impedes their adoption: a survey. J Clin Epidemiol. 2021;138:80-94.  https://doi.org/10.1016/j.jclinepi.2021.06.030  PMID: 34242757 
  5. Arno A, Elliott J, Wallace B, Turner T, Thomas J. The views of health guideline developers on the use of automation in health evidence synthesis. Syst Rev. 2021;10(1):16.  https://doi.org/10.1186/s13643-020-01569-2  PMID: 33419479 
  6. Beller E, Clark J, Tsafnat G, Adams C, Diehl H, Lund H, et al. Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR). Syst Rev. 2018;7(1):77.  https://doi.org/10.1186/s13643-018-0740-7  PMID: 29778096 
  7. Khalil H, Tamara L, Rada G, Akl EA. Challenges of evidence synthesis during the 2020 COVID pandemic: a scoping review. J Clin Epidemiol. 2022;142:10-8.  https://doi.org/10.1016/j.jclinepi.2021.10.017  PMID: 34718121 
  8. Norwegian Institute of Public Health (NIPH). Aims and strategy for the implementation of machine learning in evidence synthesis in the Cluster for Reviews and Health Technology Assessments for 2021-2022. Oslo: NIPH; 2021. Available from: https://www.fhi.no/en/publ/2021/Aims-and-strategy-for-the-implementation-of-machine-learning-in-evidence-synthesis-in-the-Cluster-for-Reviews-and-Health-Technology-Assessments-for-2021-2022/
  9. European Centre for Disease Prevention and Control (ECDC). Use and impact of new technologies for evidence synthesis. Stockholm: ECDC; 2022. Available from: https://www.ecdc.europa.eu/en/publications-data/use-and-impact-new-technologies-evidence
  10. Rai A. Explainable AI: from black box to glass box. J Acad Mark Sci. 2020;48(1):137-41.  https://doi.org/10.1007/s11747-019-00710-5 
  11. Wagner G, Lukyanenko R, Paré G. Artificial intelligence and the conduct of literature reviews. J Inf Technol. 2021;37(2).  https://doi.org/http://dx.doi.org/10.1177/02683962211048201 
  12. Abdelkader W, Navarro T, Parrish R, Cotoi C, Germini F, Iorio A, et al. Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review. JMIR Med Inform. 2021;9(9):e30401.  https://doi.org/10.2196/30401  PMID: 34499041 
  13. Bashir R, Dunn AG. Software engineering principles address current problems in the systematic review ecosystem. J Clin Epidemiol. 2019;109:136-41.  https://doi.org/10.1016/j.jclinepi.2018.12.014  PMID: 30582972 
  14. Olorisade BK, Brereton P, Andras P. Reproducibility of studies on text mining for citation screening in systematic reviews: Evaluation and checklist. J Biomed Inform. 2017;73:1-13.  https://doi.org/10.1016/j.jbi.2017.07.010  PMID: 28711679 
  15. Else H. How a torrent of COVID science changed research publishing - in seven charts. Nature. 2020;588(7839):553.  https://doi.org/10.1038/d41586-020-03564-y  PMID: 33328621 

Data & Media loading...

Submit comment
Comment moderation successfully completed
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error