Systematic Review on Healthcare Systems Engineering utilizing ChatGPT
Jungwoo Kim, Ji-Su Lee, Huijae Kim, Taesik Lee

TL;DR
This paper introduces a systematic framework using ChatGPT to analyze and categorize Healthcare Systems Engineering literature, demonstrating its potential to streamline academic reviews and reveal research trends.
Contribution
It presents a novel analytical framework employing ChatGPT for systematic reviews, organizing research into categories and analyzing trends in Healthcare Systems Engineering.
Findings
Organized field into 11 topic categories
Analyzed yearly research trends
Demonstrated ChatGPT's utility in academic reviews
Abstract
This paper presents an analytical framework for conducting academic reviews in the field of Healthcare Systems Engineering, employing ChatGPT, a state-of-the-art tool among recent language models. We utilized 9,809 abstract paragraphs from conference presentations to systematically review the field. The framework comprises distinct analytical processes, each employing tailored prompts and the systematic use of the ChatGPT API. Through this framework, we organized the target field into 11 topic categories and conducted a comprehensive analysis covering quantitative yearly trends and detailed sub-categories. This effort explores the potential for leveraging ChatGPT to alleviate the burden of academic reviews. Furthermore, it provides valuable insights into the dynamic landscape of Healthcare Systems Engineering research.
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Taxonomy
TopicsArtificial Intelligence in Healthcare · Artificial Intelligence in Healthcare and Education
