Comparison of Elicit AI and Traditional Literature Searching in Systematic Reviews using Four Case Studies

Background: Elicit AI aims to simplify and accelerate the systematic review process without compromising accuracy. However, research on Elicit’s performance is limited.Objectives: To determine whether Elicit AI is a viable tool for systematic literature searches. Methods: We compared the included studies in four systematic reviews to those identified searching with Elicit. We calculated sensitivity, precision and observed patterns in the performance of Elicit. Results: Elicit had an average of 39.6% precision (26.7% - 46.2%) which was higher than the 7.55% average of the original reviews (0.65% - 14.7%). However, the sensitivity of Elicit was poor, averaging 37.9% (25.5% - 69.2%) compared to 93.5% (87.2% - 98.0%) in the original reviews. Elicit also identified some included studies not identified by the original searches. Discussion: At the time of this evaluation, Elicit did not search with high enough sensitivity to replace traditional literature searching. However, the high precision of searching in Elicit could prove useful for preliminary searches, and the unique studies identified mean that Elicit can be used by researchers as a useful adjunct.Conclusion: Whilst Elicit searches are currently not sensitive enough to replace traditional searching, Elicit is continually improving, and further evaluations should be undertaken as new developments take place.