Jacques Cohen , Giuseppe Silvestri , Omar Paredes , Hector E. Martin-Alcala , Alejandro Chavez-Badiola , Mina Alikani , Giles A. Palmer
{"title":"Artificial intelligence in assisted reproductive technology: separating the dream from reality","authors":"Jacques Cohen , Giuseppe Silvestri , Omar Paredes , Hector E. Martin-Alcala , Alejandro Chavez-Badiola , Mina Alikani , Giles A. Palmer","doi":"10.1016/j.rbmo.2025.104855","DOIUrl":null,"url":null,"abstract":"<div><div>This paper critically reviews the role of artificial intelligence (AI) in assisted reproductive technology (ART), a nascent field that has emerged over the last decade. While AI holds immense promise for enhancing IVF efficiency, standardization, and outcomes, its current trajectory reveals significant challenges. Much of the recent literature presents variations on established methodologies rather than groundbreaking advancements, with many studies lacking clear clinical applications or outcome-driven validations. Moreover, the growing enthusiasm for AI in ART is often accompanied by undue hype that obscures its realistic potential and fosters inflated expectations. Despite these limitations, AI-driven innovations such as advanced image analysis, personalized protocols, and automation of embryology workflows are beginning to show value. Machine learning algorithms and robotics may help address inefficiencies, alleviate staff shortages, and improve decision-making in the IVF laboratory. However, progress is tempered by drawbacks including ethical concerns, limited transparency in AI systems, and regulatory impediments. Data-sharing barriers in our field hinder AI tool development significantly. Energy-intensive computational processes and expanding data centers also raise sustainability concerns, underscoring the need for environmentally responsible development. As the field evolves, it must emphasize rigorous validation, collaborative data frameworks, and alignment with the needs of ART practitioners and patients.</div></div>","PeriodicalId":21134,"journal":{"name":"Reproductive biomedicine online","volume":"50 4","pages":"Article 104855"},"PeriodicalIF":3.7000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reproductive biomedicine online","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1472648325000628","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
This paper critically reviews the role of artificial intelligence (AI) in assisted reproductive technology (ART), a nascent field that has emerged over the last decade. While AI holds immense promise for enhancing IVF efficiency, standardization, and outcomes, its current trajectory reveals significant challenges. Much of the recent literature presents variations on established methodologies rather than groundbreaking advancements, with many studies lacking clear clinical applications or outcome-driven validations. Moreover, the growing enthusiasm for AI in ART is often accompanied by undue hype that obscures its realistic potential and fosters inflated expectations. Despite these limitations, AI-driven innovations such as advanced image analysis, personalized protocols, and automation of embryology workflows are beginning to show value. Machine learning algorithms and robotics may help address inefficiencies, alleviate staff shortages, and improve decision-making in the IVF laboratory. However, progress is tempered by drawbacks including ethical concerns, limited transparency in AI systems, and regulatory impediments. Data-sharing barriers in our field hinder AI tool development significantly. Energy-intensive computational processes and expanding data centers also raise sustainability concerns, underscoring the need for environmentally responsible development. As the field evolves, it must emphasize rigorous validation, collaborative data frameworks, and alignment with the needs of ART practitioners and patients.
期刊介绍:
Reproductive BioMedicine Online covers the formation, growth and differentiation of the human embryo. It is intended to bring to public attention new research on biological and clinical research on human reproduction and the human embryo including relevant studies on animals. It is published by a group of scientists and clinicians working in these fields of study. Its audience comprises researchers, clinicians, practitioners, academics and patients.
Context:
The period of human embryonic growth covered is between the formation of the primordial germ cells in the fetus until mid-pregnancy. High quality research on lower animals is included if it helps to clarify the human situation. Studies progressing to birth and later are published if they have a direct bearing on events in the earlier stages of pregnancy.