Purpose/Significance To explore the application potential of large language models, including ChatGPT, in decision support systems for clinical translation, and to evaluate the effect of information gains from ChatGPT on summarizing research hotspots and assessing trends in male infertility studies. Method/Process The Scopus database is used to analyze the thematic differences between the literature evidence and ChatGPT virtual data in male infertility studies by using bibliometrics, topic modeling, “question-answer” consultation and other methods. Result/Conclusion The themes of male infertility research have shifted from singular to diverse, with “case discovery” ,“hormone diagnosis”, “sperm extraction” and “genetic marker identification” becoming research hotspots. Using ChatGPT can supplement bibliometric and topic modeling evidence, and effectively explore the hotspots and trends of male infertility research.