The Landscape of Wearable Sensors and Automated Literature Analysis with Large-Language Models

The rapid growth of scientific literature demands advanced methodologies to analyze and synthesize research trends efficiently. This paper explores the integration of complex network analysis and large language models (LLMs) to automate the generation of literature analyses, focusing on the field of wearable sensors for health monitoring. Using OpenAlex as a source of scientific papers in this field, paper citation networks were constructed and partitioned into thematic clusters, revealing key subtopics such as flexible graphene-based sensors, gait analysis, and machine learning applications. These clusters, characterized by their term importance and interconnectivity, served as input for LLMs (ChatGPT) to generate structured outlines and descriptive summaries. While LLMs produced coherent overviews, limitations emerged, including superficial analyses and inaccuracies in referenced literature. The study demonstrates the potential of combining network-based methodologies with LLMs to create scalable literature reviews, albeit with limitations to be addressed concerning depth and accuracy. The analyses highlight wearable sensors' transformative role in healthcare, driven by advancements in materials science, artificial intelligence, and device integration, while also identifying critical gaps such as standardization, biocompatibility, and energy efficiency. This hybrid approach offers a promising framework for accelerating scholarly synthesis, though today human oversight remains essential to ensure rigor and relevance.