White paper

Artificial Intelligence in Systematic Literature Reviews Part 4 | AI-enabled Initial Data Extraction

In the first two papers in this series on AI in systematic literature reviews (SLRs), we presented our methodology and results for testing the performance of AI models in title/abstract (TiAB) and full-text screening (FTS), with which we achieved high sensitivity (up to 96% for TiAB screening, and ≥99% for FTS). In this paper, we report our methodology and findings on the subsequent SLR step, initial data extraction, using the OpenAI o1-mini model. Overall, the model demonstrated consistently high accuracy and sensitivity (≥90% for most variables) across our validation datasets. These results also indicate the model is likely to perform very well in full-scale extraction (the subject of our next white paper due to be released within the next few weeks). 

Link to White Paper