Identification and Categorization of the Top 100 Articles and the Future of Large Language Models: Thematic Analysis...

Since the release of ChatGPT and other large language models (LLMs), there has been a significant increase in academic publications exploring their capabilities and implications across various fields, such as medicine, education, and technology.

This study aims to identify the most influential academic works on LLMs published in the past year, categorize their research types and thematic focuses, within different professional fields. The study also evaluates the ability of artificial intelligence (AI) tools, such as ChatGPT, to accurately classify academic research.

We conducted a bibliometric analysis using Clarivate's Web of Science (WOS) to extract the top 100 most cited papers on LLMs. Papers were manually categorized by field, journal, author, and research type. ChatGPT-4 was used to generate categorizations for the same papers, and its performance was compared to human classifications. We summarized the distribution of research fields and assessed the concordance between AI-generated and manual classifications.

Medicine emerged as the predominant field among the top 100 most cited papers, accounting for 43 (43%), followed by education 26 (26%) and technology 15 (15%). Medical literature primarily focused on clinical applications of LLMs, limitations of AI in health care, and the role of AI in medical education. In education, research was centered around ethical concerns and potential applications of AI for teaching and learning. ChatGPT demonstrated variable concordance with human reviewers, achieving an agreement rating of 47% for research types and 92% for fields of study.

While LLMs such as ChatGPT exhibit considerable potential in aiding research categorization, human oversight remains essential to address issues such as hallucinations, outdated information, and biases in AI-generated outputs. This study highlights the transformative potential of LLMs across multiple sectors and emphasizes the importance of continuous ethical evaluation and iterative improvement of AI systems to maximize their benefits while minimizing risks.