Unveiling Male Infertility Research Hotspots: Leveraging ChatGPT's Information Enhancement for Clinical Translation (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Infertility is a significant negative factor affecting societal population growth and economic stability, with male infertility being a major cause of infertility. In recent years, with the development and advancement of next-generation sequencing technologies and high-resolution mass spectrometry, the volume of male infertility-related literature in scientific databases such as Scopus and PubMed has rapidly increased, and its topics have undergone complex changes over the past 50 years. Additionally, the advent of large language models (like ChatGPT) has provided new tools for enhancing traditional literature analysis and topic modeling. </sec> <sec> <title>OBJECTIVE</title> This research study aims to explore the potential of large language models, such as ChatGPT, in decision support systems for the clinical translation of male infertility research. </sec> <sec> <title>METHODS</title> Various methods, including bibliometrics, topic modeling, and ChatGPT's question-answer approach, were employed to compare male infertility hotspots between real-world and virtual-world data. Additionally, the study investigated ChatGPT's ability to enhance information in summarizing male infertility hotspots. </sec> <sec> <title>RESULTS</title> Under the literature evidence of 14,478 male infertility-related papers (12,534 research papers and 1,944 review papers), traditional bibliometric analyses such as annual analysis, country analysis, and high-impact author analysis show that countries like the United States, China, and Italy are major publishers in infertility research, with the United States being the leading technical influencer in male infertility research. Subsequently, results from topic modeling analysis have effectively mapped out the research themes in male infertility over the past 50 years, this analysis highlights key subjects such as 'the impact of gene expression on male infertility', 'the effect of age on sperm parameters', and 'pathogenic genes of male infertility', marking them as recent research hotspots. However, this method falls short in clearly presenting the latest hotspots in male infertility research. Lastly, the integration of ChatGPT information enhancement offers a new dimension in this research. This approach successfully presents the recent hotspots in male infertility, encompassing not only the impact of risk factors like 'Environmental Exposures', 'Genetics', 'Immunological Factors', 'Hormonal Imbalances' on sperm count and quality but also highlighting emerging areas such as 'Precision Medicine' and 'Artificial Intelligence (AI)' in male infertility research. </sec> <sec> <title>CONCLUSIONS</title> Therefore, combining real-world literature evidence with the capabilities of ChatGPT is crucial for understanding and mapping future trends in this field. </sec> <sec> <title>CLINICALTRIAL</title> Trial Registration: Not applicable </sec>

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