AI‐Assisted Literature Review: Integrating Visualization and Geometric Features for Insightful Analysis

Abstract

Rapid advancements in technology and Artificial Intelligence have increased the volume of scientific research, making it challenging for researchers and scholars to keep pace with the evolving literature and state‐of‐the‐art techniques and methods. Traditional review papers offer a way to mitigate these difficulties but are often time‐consuming and labor‐intensive. This article introduces a novel AI‐assisted narrative review methodology that integrates advanced text retrieval and visualization techniques, enhanced with geometric features, to address this. The proposed approach relies on the automatic identification of research topics/clusters within a large different document corpus of different time periods. This approach not only facilitates the systematic exploration of trends over time but also serves as a valuable adjunct, enabling experts to focus on specific, homogeneous areas within scientific fields/clusters. Initially, the methodology in its generality and mapping of the evolution of emerging topics are described, revealing the temporal dynamics and interconnections within the literature of time series anomalies. Subsequently, the proposed method is applied to time series data and an in‐depth exploration of the identified dominant cluster is presented. The cluster involves advanced techniques and models for anomaly detection in time series analysis. Focusing on such a homogeneous subfield enables the derivation of a wealth of characteristics and outcomes regarding the evolution of this topic, revealing its temporal dynamics and trends. The review process demonstrates the effectiveness of the proposed AI‐driven approach in literature reviews and provides researchers with a powerful tool to synthesize and interpret complex, dynamically changing, advanced scientific fields.