The Screening Bottleneck You've crafted the perfect search string, only to be met with thousands of results. Manually screening titles and abstracts is a monumental, time-consuming task that delays the real research. What if AI could learn your inclusion criteria and do the heavy lifting? The Core Principle: Active Learning The most effective framework for this automation is active learning . Instead of a static model, it's an interactive loop. You start by screening a small, random batch of records. The AI model learns from your decisions and then intelligently selects the next most uncertain records for you to review. This continuous feedback allows the system to rapidly identify the relevant literature, often finding the majority of included studies after you've screened only a fraction of the total. A Tool in Action: ASReview Platforms like ASReview are built on this principle. It implements the uncertainty sampling query strategy.…