Loon Lens 1.0 Validation: Agentic AI for Title and Abstract Screening in Systematic Literature Reviews

IntroductionSystematic literature reviews (SLRs) are critical for informing clinical research and practice, but they are time-consuming and resource-intensive, particularly during Title and Abstract (TiAb) screening. Loon Lens, an autonomous, agentic AI platform, streamlines TiAb screening without the need for human reviewers to conduct any screening. MethodsThis study validates Loon Lens against human reviewer decisions across eight SLRs conducted by Canadas Drug Agency, covering a range of drugs and eligibility criteria. A total of 3,796 citations were retrieved, with human reviewers identifying 287 (7.6%) for inclusion. Loon Lens autonomously screened the same citations based on the provided inclusion and exclusion criteria. Metrics such as accuracy, recall, precision, F1 score, specificity, and negative predictive value (NPV) were calculated. Bootstrapping was applied to compute 95% confidence intervals. ResultsLoon Lens achieved an accuracy of 95.5% (95% CI: 94.8-96.1), with recall at 98.95% (95% CI: 97.57-100%) and specificity at 95.24% (95% CI: 94.54-95.89%). Precision was lower at 62.97% (95% CI: 58.39-67.27%), suggesting that Loon Lens included more citations for full-text screening compared to human reviewers. The F1 score was 0.770 (95% CI: 0.734-0.802), indicating a strong balance between precision and recall. ConclusionLoon Lens demonstrates the ability to autonomously conduct TiAb screening with a substantial potential for reducing the time and cost associated with manual or semi-autonomous TiAb screening in SLRs. While improvements in precision are needed, the platform offers a scalable, autonomous solution for systematic reviews. Access to Loon Lens is available upon request at https://loonlens.com/.