An Overview On Large Language Models Across Key Domains: A Systematic Review

Mattia Bruscia, Graziano A. Manduzio, Federico A. Galatolo, Mario G. C. A. Cimino, Alberto Greco, Lorenzo Cominelli, Enzo Pasquale Scilingo

Abstract: This systematic review explores the applications of Large Language Models (LLMs) across a variety of academic disciplines and professional fields. The analysis is structured through a methodical examination of data derived from the Scopus database over the period from 2017 to 2024. We created both annual and comprehensive datasets of articles and related information based on a generic query, which allowed us to track the development and integration of LLMs into different application fields. To this end, we conceived a dedicated approach that includes an analysis of the trends of subject areas and a Pertinence Analysis (PA) to filter out articles that are not genuinely related to LLMs. Additionally, we performed an annual and overall Terminological Relevance Analysis (TRA) using Machine Learning (ML) techniques, and we examined the yearly trends in research areas containing LLM-related articles by observing the relevance indicators of emerging terms. This extensive investigation highlights how LLMs are increasingly being utilized to improve efficiency, accuracy and productivity, particularly in health and healthcare care, guiding the responsible advancement and application of these technologies in sensitive domains.