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Three ways of knowing: the integration of clinical expertise, evidence-based medicine, and artificial intelligence in assisted reproductive technologies.三种认知方式:临床专业知识、循证医学和人工智能在辅助生殖技术中的整合。
J Assist Reprod Genet. 2021 Jul;38(7):1617-1625. doi: 10.1007/s10815-021-02159-4. Epub 2021 Apr 19.
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Moonshot. Long shot. Or sure shot. What needs to happen to realize the full potential of AI in the fertility sector?登月计划。远射。还是必进之球。要充分挖掘人工智能在生育领域的潜力,需要发生什么?
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Artificial intelligence and assisted reproductive technologies: 2023. Ready for prime time? Or not.人工智能与辅助生殖技术:2023 年。是否已准备好迎来黄金时代?
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Impact of artificial intelligence on prognosis, shared decision-making, and precision medicine for patients with inflammatory bowel disease: a perspective and expert opinion.人工智能对炎症性肠病患者预后、共同决策和精准医学的影响:观点和专家意见。
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Current applications of artificial intelligence in assisted reproductive technologies through the perspective of a patient's journey.人工智能在辅助生殖技术中的当前应用——从患者旅程的角度。
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AI for One Welfare: the role of animal welfare scientists in developing valid and ethical AI-based welfare assessment tools.人工智能促进整体福利:动物福利科学家在开发基于人工智能的有效且符合伦理的福利评估工具中的作用。
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At-home urine estrone-3-glucuronide quantification predicts oocyte retrieval outcomes comparably with serum estradiol.在家进行尿雌酮-3-葡萄糖醛酸苷定量检测预测卵母细胞取卵结果与血清雌二醇相当。
F S Rep. 2023 Jan 28;4(1):43-48. doi: 10.1016/j.xfre.2023.01.006. eCollection 2023 Mar.
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Proceedings of the first world conference on AI in fertility.第一届人工智能在生育领域世界大会会议记录
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本文引用的文献

1
Data sharing: using blockchain and decentralized data technologies to unlock the potential of artificial intelligence: What can assisted reproduction learn from other areas of medicine?数据共享:利用区块链和去中心化数据技术释放人工智能的潜力:辅助生殖可以从医学的其他领域学到什么?
Fertil Steril. 2020 Nov;114(5):927-933. doi: 10.1016/j.fertnstert.2020.09.160.
2
Precision medicine and artificial intelligence: overview and relevance to reproductive medicine.精准医学与人工智能:概述及其与生殖医学的相关性。
Fertil Steril. 2020 Nov;114(5):908-913. doi: 10.1016/j.fertnstert.2020.09.156.
3
Artificial intelligence in in vitro fertilization: a computer decision support system for day-to-day management of ovarian stimulation during in vitro fertilization.人工智能在体外受精中的应用:一个计算机决策支持系统,用于体外受精过程中卵巢刺激的日常管理。
Fertil Steril. 2020 Nov;114(5):1026-1031. doi: 10.1016/j.fertnstert.2020.06.006. Epub 2020 Oct 1.
4
Performance of a deep learning based neural network in the selection of human blastocysts for implantation.基于深度学习的神经网络在选择人类囊胚进行植入中的性能。
Elife. 2020 Sep 15;9:e55301. doi: 10.7554/eLife.55301.
5
Prediction Models - Development, Evaluation, and Clinical Application.预测模型——开发、评估与临床应用。
N Engl J Med. 2020 Apr 23;382(17):1583-1586. doi: 10.1056/NEJMp2000589.
6
Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF.开发一种基于人工智能的评估模型,用于通过体外受精期间光学显微镜拍摄的静态图像预测胚胎活力。
Hum Reprod. 2020 Apr 28;35(4):770-784. doi: 10.1093/humrep/deaa013.
7
Artificial Intelligence in Health Care: A Report From the National Academy of Medicine.《医疗保健中的人工智能:美国国家医学院的一份报告》
JAMA. 2020 Feb 11;323(6):509-510. doi: 10.1001/jama.2019.21579.
8
Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.基于证据的临床决策支持系统用于危重症中三种疾病状态的预测与检测:一项系统文献综述
F1000Res. 2019 Oct 8;8:1728. doi: 10.12688/f1000research.20498.2. eCollection 2019.
9
Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis.使用临床推理本体论构建更智能的临床决策支持系统:系统评价和数据综合。
J Am Med Inform Assoc. 2020 Jan 1;27(1):159-174. doi: 10.1093/jamia/ocz169.
10
Prioritizing Features to Redesign in an EMR System.确定电子病历系统中需重新设计的功能的优先级。
Stud Health Technol Inform. 2019 Aug 21;264:1213-1217. doi: 10.3233/SHTI190419.

三种认知方式:临床专业知识、循证医学和人工智能在辅助生殖技术中的整合。

Three ways of knowing: the integration of clinical expertise, evidence-based medicine, and artificial intelligence in assisted reproductive technologies.

机构信息

Seattle Reproductive Medicine, 1505 Westlake Avenue, Suite 400, Seattle, WA, 98104, USA.

出版信息

J Assist Reprod Genet. 2021 Jul;38(7):1617-1625. doi: 10.1007/s10815-021-02159-4. Epub 2021 Apr 19.

DOI:10.1007/s10815-021-02159-4
PMID:33870475
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8324699/
Abstract

Decision-making in fertility care is on the cusp of a significant frameshift. Online tools to integrate artificial intelligence into the decision-making process across all aspects of ART are rapidly emerging. These tools have the potential to improve outcomes and transition decision-making from one based on traditional provider centric assessments toward a hybrid triad of expertise, evidence, and algorithmic data analytics using AI. We can look forward to a time when AI will be the third part of a provider's tool box to complement expertise and medical literature to enable ever more accurate predictions and outcomes in ART. In their fully integrated format, these tools will be part of a digital fertility ecosystem of analytics embedded within an EMR. To date, the impact of AI on ART outcomes is inconclusive. No prospective studies have shown clear cut benefit or cost reductions over current practices, but we are very early in the process of developing and evaluating these tools. We owe it to ourselves to begin to examine these AI-driven analytics and develop a very clear idea about where we can and should go before we roll these tools into clinical care. Thoughtful scrutiny is essential lest we find ourselves in a position of trying to modulate and modify after entry of these tools into our clinics and patient care. The purpose of this commentary is to highlight the evolution and impact AI has had in other fields relevant to the fertility sector and describe a vision for applications within ART that could improve outcomes, reduce costs, and positively impact clinical care.

摘要

生育护理中的决策正处于重大转变的边缘。将人工智能集成到 ART 各个方面决策过程中的在线工具正在迅速涌现。这些工具有可能改善结果,并将决策从基于传统提供者为中心的评估转变为专家、证据和使用 AI 的算法数据分析的混合三位一体。我们可以期待有一天,人工智能将成为提供者工具包的第三部分,以补充专业知识和医学文献,从而在 ART 中实现更准确的预测和结果。在其完全集成的形式下,这些工具将成为嵌入电子病历 (EMR) 的数字生育生态系统分析的一部分。到目前为止,人工智能对 ART 结果的影响尚无定论。没有前瞻性研究表明在当前实践基础上有明显的获益或成本降低,但我们还处于开发和评估这些工具的早期阶段。在将这些工具引入临床护理之前,我们有责任开始研究这些人工智能驱动的分析,并清楚地了解我们可以并且应该在哪些方面进行。审慎的审查至关重要,以免我们发现自己处于这些工具进入我们的诊所和患者护理后试图调节和修改的境地。本评论的目的是强调人工智能在与生育领域相关的其他领域中的发展和影响,并描述在 ART 中应用的愿景,这些应用有可能改善结果、降低成本并对临床护理产生积极影响。