From Theory to Practice: Implementing AI Screening for Literature Reviews

AI-powered screening tools are transforming the tedious screening stage of systematic reviews by leveraging active learning. This interactive approach ensures expertise is applied where the AI needs it most, reducing the number of records to be screened manually. By using uncertainty sampling, researchers can stop screening after reviewing only 10-20% of the total dataset. To implement AI screening, prepare and import data, train the model with initial screening, and review in priority order. This approach optimizes researcher judgment, not replaces it.