{"title":"人工智能在医学中的潜力及其在男性不育症中的应用。","authors":"Hideyuki Kobayashi","doi":"10.1002/rmb2.12590","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The third AI boom, which began in 2010, has been characterized by the rapid evolution and diversification of AI and marked by the development of key technologies such as machine learning and deep learning. AI is revolutionizing the medical field, enhancing diagnostic accuracy, surgical outcomes, and drug production.</p><p><strong>Methods: </strong>This review includes explanations of digital transformation (DX), the history of AI, the difference between machine learning and deep learning, recent AI topics, medical AI, and AI research in male infertility.</p><p><strong>Main findings results: </strong>In research on male infertility, I established an AI-based prediction model for Johnsen scores and an AI predictive model for sperm retrieval in non-obstructive azoospermia, both by no-code AI.</p><p><strong>Conclusions: </strong>AI is making constant progress. It would be ideal for physicians to acquire a knowledge of AI and even create AI models. No-code AI tools have revolutionized model creation, allowing individuals to independently handle data preparation and model development. Previously a team effort, this shift empowers users to craft customized AI models solo, offering greater flexibility and control in the model creation process.</p>","PeriodicalId":21116,"journal":{"name":"Reproductive Medicine and Biology","volume":"23 1","pages":"e12590"},"PeriodicalIF":2.7000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211808/pdf/","citationCount":"0","resultStr":"{\"title\":\"Potential for artificial intelligence in medicine and its application to male infertility.\",\"authors\":\"Hideyuki Kobayashi\",\"doi\":\"10.1002/rmb2.12590\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The third AI boom, which began in 2010, has been characterized by the rapid evolution and diversification of AI and marked by the development of key technologies such as machine learning and deep learning. AI is revolutionizing the medical field, enhancing diagnostic accuracy, surgical outcomes, and drug production.</p><p><strong>Methods: </strong>This review includes explanations of digital transformation (DX), the history of AI, the difference between machine learning and deep learning, recent AI topics, medical AI, and AI research in male infertility.</p><p><strong>Main findings results: </strong>In research on male infertility, I established an AI-based prediction model for Johnsen scores and an AI predictive model for sperm retrieval in non-obstructive azoospermia, both by no-code AI.</p><p><strong>Conclusions: </strong>AI is making constant progress. It would be ideal for physicians to acquire a knowledge of AI and even create AI models. No-code AI tools have revolutionized model creation, allowing individuals to independently handle data preparation and model development. Previously a team effort, this shift empowers users to craft customized AI models solo, offering greater flexibility and control in the model creation process.</p>\",\"PeriodicalId\":21116,\"journal\":{\"name\":\"Reproductive Medicine and Biology\",\"volume\":\"23 1\",\"pages\":\"e12590\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211808/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Reproductive Medicine and Biology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/rmb2.12590\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"OBSTETRICS & GYNECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reproductive Medicine and Biology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/rmb2.12590","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
Potential for artificial intelligence in medicine and its application to male infertility.
Background: The third AI boom, which began in 2010, has been characterized by the rapid evolution and diversification of AI and marked by the development of key technologies such as machine learning and deep learning. AI is revolutionizing the medical field, enhancing diagnostic accuracy, surgical outcomes, and drug production.
Methods: This review includes explanations of digital transformation (DX), the history of AI, the difference between machine learning and deep learning, recent AI topics, medical AI, and AI research in male infertility.
Main findings results: In research on male infertility, I established an AI-based prediction model for Johnsen scores and an AI predictive model for sperm retrieval in non-obstructive azoospermia, both by no-code AI.
Conclusions: AI is making constant progress. It would be ideal for physicians to acquire a knowledge of AI and even create AI models. No-code AI tools have revolutionized model creation, allowing individuals to independently handle data preparation and model development. Previously a team effort, this shift empowers users to craft customized AI models solo, offering greater flexibility and control in the model creation process.
期刊介绍:
Reproductive Medicine and Biology (RMB) is the official English journal of the Japan Society for Reproductive Medicine, the Japan Society of Fertilization and Implantation, the Japan Society of Andrology, and publishes original research articles that report new findings or concepts in all aspects of reproductive phenomena in all kinds of mammals. Papers in any of the following fields will be considered: andrology, endocrinology, oncology, immunology, genetics, function of gonads and genital tracts, erectile dysfunction, gametogenesis, function of accessory sex organs, fertilization, embryogenesis, embryo manipulation, pregnancy, implantation, ontogenesis, infectious disease, contraception, etc.