How to conduct efficient screening in literature retrieval through AI?

AI-driven literature screening employs natural language processing and machine learning to automate the identification of relevant studies, significantly accelerating review processes while maintaining rigor when integrated into a structured workflow. It is feasible and increasingly reliable for high-volume retrieval tasks. Effective implementation requires well-defined inclusion/exclusion criteria translated into precise algorithmic queries. Training datasets must be representative and rigorously annotated to optimize model accuracy. Choosing appropriate algorithms depends on the task: classification models (e.g., SVM, transformers like BERT) filter records, while NLP techniques extract key concepts. Crucially, validation against human screening and iterative model refinement are essential to mitigate bias and ensure recall/precision targets are met. Human oversight remains vital for resolving ambiguities and adjudicating borderline cases. To implement, first codify screening criteria into structured rules or labeled training data. Pilot the AI tool on a subset of records, calibrating thresholds based on performance metrics like F1-score. Run the validated tool on the full dataset, flagging records for human verification where confidence scores are low. This hybrid approach reduces manual screening burden by 30-70%, enabling scalable systematic reviews and rapid evidence synthesis essential for timely research and decision-making.