The Use of Artificial Intelligence in Dermatology Systematic Reviews: A Comparative Analysis of Elicit Against Human Reviewers

Authors

  • Jui Vyas 1Centre for Medical Education, School of Medicine, Cardiff University, Cardiff, United Kingdom https://orcid.org/0000-0003-2839-2651
  • Jeffrey R. Johns Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, CF14 4XN, United Kingdom https://orcid.org/0000-0002-1628-4306
  • Emily Forrest School of Medicine, Cardiff University, Cardiff, CF14 4XN, United Kingdom
  • Mari Ann Hilliar Health Library, University Library Service, Cardiff University, Cardiff, CF14 4YU, United Kingdom
  • Sam Salek School of Health, Medicine and Life Sciences, University of Hertfordshire, Hatfield, AL10 9AB, United Kingdom
  • Andrew Y. Finlay Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, CF14 4XN, United Kingdom https://orcid.org/0000-0003-2143-1646

DOI:

https://doi.org/10.2340/actadv.v106.adv-2025-0213

Keywords:

artificial intelligence, Elicit, dermatology

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References

Borah R, Brown AW, Capers PL, Kaiser KA. Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry. BMJ Open 2017; 7: e012545. DOI: https://doi.org/10.1136/bmjopen-2016-012545

Khalil H, Ameen D, Zarnegar A. Tools to support the automation of systematic reviews: a scoping review. J Clin Epidemiol 2022; 144: 22–42. DOI: https://doi.org/10.1016/j.jclinepi.2021.12.005

Bernard N, Sagawa Y Jr, Bier N, Lihoreau T, Pazart L, Tannou T. Using artificial intelligence for systematic review: the example of elicit. BMC Med Res Methodol 2025; 25: 75. DOI: https://doi.org/10.1186/s12874-025-02528-y

Johns JR, Vyas J, Ali FM, Ingram JR, Salek S, Finlay AY. The Dermatology Life Quality Index as the primary outcome in randomized clinical trials: a systematic review. Br J Dermatol 2024; 191: 497–507. DOI: https://doi.org/10.1093/bjd/ljae228

Stephen HH, Douglas MJ. Appendix: Jadad scale for reporting randomized controlled trials. Evidence-based Obstetric Anesthesia: Blackwell Publishing Ltd; 2005.

Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJ, Gavaghan DJ, et al. Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials 1996; 17: 1–12. DOI: https://doi.org/10.1016/0197-2456(95)00134-4

Gusenbauer M. Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases. Scientometrics 2019; 118: 177–214. DOI: https://doi.org/10.1007/s11192-018-2958-5

Ioannidis JPA. Why most published research findings are false. PLoS Med 2005; 2: e124. DOI: https://doi.org/10.1371/journal.pmed.0020124

Vetter TR, Mascha EJ. Defining the primary outcomes and justifying secondary outcomes of a study: usually, the fewer, the better. Anesth Analg 2017; 125: 678–681. DOI: https://doi.org/10.1213/ANE.0000000000002224

Published

2026-03-23

How to Cite

Vyas, J., Johns, J. R., Forrest, E., Hilliar, M. A., Salek, S., & Finlay, A. Y. (2026). The Use of Artificial Intelligence in Dermatology Systematic Reviews: A Comparative Analysis of Elicit Against Human Reviewers. Acta Dermato-Venereologica, 106, adv–2025. https://doi.org/10.2340/actadv.v106.adv-2025-0213

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Short Communication

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