{"title":"The potential of large language models in the field of infertility: a systematic review.","authors":"Wei Li, Attiq Ur-Rehman, Meng-Wei Ge, Lu-Ting Shen, Xi-Yuan Peng, Kang Zhong, Rui Feng, SiQi Gao, Fei-Hong Hu, Yi-Jie Jia, Hong-Lin Chen","doi":"10.1007/s10815-025-03679-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Large language models have demonstrated significant potential across a wide range of medical fields and may potentially induce a transformation in the field of infertility and sterility. This systematic review aims to synthesize existing research and explore the current strengths and limitations of large language models in the field of infertility and sterility.</p><p><strong>Methods: </strong>Researchers conducted a comprehensive search across three databases and employed a thematic synthesis approach for data analysis.</p><p><strong>Results: </strong>The analysis included a total of 13 studies. Large language models exhibited advantages in the accuracy and reproducibility of information output and consultation, demonstrated robust learning capabilities, and were able to provide satisfactory recommendations to patients. However, there were significant variations in the performance of different large language models, and the readability of the output information was poor, making it difficult to provide comprehensive answers.</p><p><strong>Conclusion: </strong>In the domain of infertility, large language models have not yet achieved full reliability and should be regarded solely as information sources whose outputs require careful verification; they are presently incapable of substituting for clinical diagnosis. Their development requires increased investment in relevant technologies and the use of authoritative, accurate information to unlock their potential in this field and other medical areas.</p>","PeriodicalId":15246,"journal":{"name":"Journal of Assisted Reproduction and Genetics","volume":" ","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Assisted Reproduction and Genetics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10815-025-03679-z","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: Large language models have demonstrated significant potential across a wide range of medical fields and may potentially induce a transformation in the field of infertility and sterility. This systematic review aims to synthesize existing research and explore the current strengths and limitations of large language models in the field of infertility and sterility.
Methods: Researchers conducted a comprehensive search across three databases and employed a thematic synthesis approach for data analysis.
Results: The analysis included a total of 13 studies. Large language models exhibited advantages in the accuracy and reproducibility of information output and consultation, demonstrated robust learning capabilities, and were able to provide satisfactory recommendations to patients. However, there were significant variations in the performance of different large language models, and the readability of the output information was poor, making it difficult to provide comprehensive answers.
Conclusion: In the domain of infertility, large language models have not yet achieved full reliability and should be regarded solely as information sources whose outputs require careful verification; they are presently incapable of substituting for clinical diagnosis. Their development requires increased investment in relevant technologies and the use of authoritative, accurate information to unlock their potential in this field and other medical areas.
期刊介绍：
The Journal of Assisted Reproduction and Genetics publishes cellular, molecular, genetic, and epigenetic discoveries advancing our understanding of the biology and underlying mechanisms from gametogenesis to offspring health. Special emphasis is placed on the practice and evolution of assisted reproduction technologies (ARTs) with reference to the diagnosis and management of diseases affecting fertility. Our goal is to educate our readership in the translation of basic and clinical discoveries made from human or relevant animal models to the safe and efficacious practice of human ARTs. The scientific rigor and ethical standards embraced by the JARG editorial team ensures a broad international base of expertise guiding the marriage of contemporary clinical research paradigms with basic science discovery. JARG publishes original papers, minireviews, case reports, and opinion pieces often combined into special topic issues that will educate clinicians and scientists with interests in the mechanisms of human development that bear on the treatment of infertility and emerging innovations in human ARTs. The guiding principles of male and female reproductive health impacting pre- and post-conceptional viability and developmental potential are emphasized within the purview of human reproductive health in current and future generations of our species.
The journal is published in cooperation with the American Society for Reproductive Medicine, an organization of more than 8,000 physicians, researchers, nurses, technicians and other professionals dedicated to advancing knowledge and expertise in reproductive biology.
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