How to use AI for intelligent ranking and priority ranking of literature?

AI facilitates intelligent literature ranking through relevance scoring and prioritization algorithms, enabling researchers to identify seminal papers efficiently. This application is both technically feasible and increasingly adopted for managing information overload. Essential principles involve feature extraction from texts, including keywords, citations, methodology, publication venue impact, and author prominence. Machine learning models, particularly supervised learning for relevance prediction or unsupervised techniques for thematic clustering, generate ranking scores. Effectiveness depends heavily on training data quality and the precise definition of ranking objectives (e.g., novelty, methodological rigor). Users must validate algorithmic outputs against expert judgment to mitigate inherent biases. Implementation begins by defining specific ranking criteria aligned with research goals. Users then select or train appropriate AI models using curated literature datasets. Once trained, the system ingests new articles or literature corpora, automatically scoring and prioritizing them. This integration significantly accelerates literature review workflows, reduces manual screening time, surfaces critical research gaps based on emerging topics or highly cited works, and aids systematic review processes by presenting the most pertinent studies first.