{"title":"Moonshot. Long shot. Or sure shot. What needs to happen to realize the full potential of AI in the fertility sector?","authors":"Gerard Letterie","doi":"10.1093/humrep/deae144","DOIUrl":null,"url":null,"abstract":"<p><p>Quality healthcare requires two critical components: patients' best interests and best decisions to achieve that goal. The first goal is the lodestar, unchanged and unchanging over time. The second component is a more dynamic and rapidly changing paradigm in healthcare. Clinical decision-making has transitioned from an opinion-based paradigm to an evidence-based and data-driven process. A realization that technology and artificial intelligence can bring value adds a third component to the decision process. And the fertility sector is not exempt. The debate about AI is front and centre in reproductive technologies. Launching the transition from a conventional provider-driven decision paradigm to a software-enhanced system requires a roadmap to enable effective and safe implementation. A key nodal point in the ascending arc of AI in the fertility sector is how and when to bring these innovations into the ART routine to improve workflow, outcomes, and bottom-line performance. The evolution of AI in other segments of clinical care would suggest that caution is needed as widespread adoption is urged from several fronts. But the lure and magnitude for the change that these tech tools hold for fertility care remain deeply engaging. Exploring factors that could enhance thoughtful implementation and progress towards a tipping point (or perhaps not) should be at the forefront of any 'next steps' strategy. The objective of this Opinion is to discuss four critical areas (among many) considered essential to successful uptake of any new technology. These four areas include value proposition, innovative disruption, clinical agency, and responsible computing.</p>","PeriodicalId":13003,"journal":{"name":"Human reproduction","volume":" ","pages":"1863-1868"},"PeriodicalIF":6.0000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human reproduction","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1093/humrep/deae144","RegionNum":1,"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
Quality healthcare requires two critical components: patients' best interests and best decisions to achieve that goal. The first goal is the lodestar, unchanged and unchanging over time. The second component is a more dynamic and rapidly changing paradigm in healthcare. Clinical decision-making has transitioned from an opinion-based paradigm to an evidence-based and data-driven process. A realization that technology and artificial intelligence can bring value adds a third component to the decision process. And the fertility sector is not exempt. The debate about AI is front and centre in reproductive technologies. Launching the transition from a conventional provider-driven decision paradigm to a software-enhanced system requires a roadmap to enable effective and safe implementation. A key nodal point in the ascending arc of AI in the fertility sector is how and when to bring these innovations into the ART routine to improve workflow, outcomes, and bottom-line performance. The evolution of AI in other segments of clinical care would suggest that caution is needed as widespread adoption is urged from several fronts. But the lure and magnitude for the change that these tech tools hold for fertility care remain deeply engaging. Exploring factors that could enhance thoughtful implementation and progress towards a tipping point (or perhaps not) should be at the forefront of any 'next steps' strategy. The objective of this Opinion is to discuss four critical areas (among many) considered essential to successful uptake of any new technology. These four areas include value proposition, innovative disruption, clinical agency, and responsible computing.
优质医疗服务需要两个关键要素:患者的最佳利益和实现这一目标的最佳决策。第一个目标是长期不变的标准。第二个要素则是医疗保健中更动态、更快速变化的范式。临床决策已从基于意见的模式过渡到基于证据和数据的流程。人们认识到技术和人工智能可以为决策过程带来价值,这为决策过程增添了第三个组成部分。生育领域也不例外。关于人工智能的讨论是生殖技术领域的焦点。要从传统的由医疗服务提供者驱动的决策模式过渡到软件增强型系统,需要制定一个路线图,以便有效、安全地实施。人工智能在生育领域的上升弧线中的一个关键节点是,如何以及何时将这些创新引入 ART 常规,以改善工作流程、结果和底线绩效。人工智能在其他临床护理领域的发展表明,在多方面敦促广泛采用人工智能的同时,还需要谨慎行事。但是,这些技术工具对生育保健的诱惑和变革的幅度仍然令人深感兴趣。在任何 "下一步 "战略中,最重要的是探索能促进深思熟虑的实施和迈向临界点(或许不会)的因素。本《意见书》旨在讨论被认为对任何新技术的成功应用都至关重要的四个关键领域(在众多领域中)。这四个方面包括价值主张、创新颠覆、临床代理和负责任的计算。
期刊介绍:
Human Reproduction features full-length, peer-reviewed papers reporting original research, concise clinical case reports, as well as opinions and debates on topical issues.
Papers published cover the clinical science and medical aspects of reproductive physiology, pathology and endocrinology; including andrology, gonad function, gametogenesis, fertilization, embryo development, implantation, early pregnancy, genetics, genetic diagnosis, oncology, infectious disease, surgery, contraception, infertility treatment, psychology, ethics and social issues.