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[医学与妇科领域的人工智能——误入歧途还是治愈希望?]

[Artificial intelligence in medicine and gynecology-the wrong track or promise of cure?].

作者信息

Sonntag Daniel

机构信息

Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Universität Oldenburg, Marie-Curie-Str. 1, 26129 Oldenburg, Deutschland.

出版信息

Gynakologe. 2021;54(7):476-482. doi: 10.1007/s00129-021-04808-2. Epub 2021 May 6.

DOI:10.1007/s00129-021-04808-2
PMID:33972805
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8100931/
Abstract

Artificial intelligence (AI) has attained a new level of maturity in recent years and is becoming the driver of digitalization in all areas of life. AI is a cross-sectional technology with great importance for all areas of medicine employing image data, text data and bio-data. There is no medical field that will remain unaffected by AI, with AI-assisted clinical decision-making assuming a particularly important role. AI methods are becoming established in medical workflow management and for prediction of treatment success or treatment outcome. AI systems are already able to lend support to imaging-based diagnosis and patient management, but cannot suggest critical decisions. The corresponding preventive or therapeutic measures can be more rationally assessed with the help of AI, although the number of diseases covered is currently too low to create robust systems for routine clinical use. Prerequisite for the widespread use of AI systems is appropriate training to enable physicians to decide when computer-assisted decision-making can be relied upon.

摘要

近年来,人工智能(AI)已达到新的成熟水平,并正成为生活各领域数字化的驱动力。人工智能是一项具有跨领域性质的技术,对所有使用图像数据、文本数据和生物数据的医学领域都极为重要。没有哪个医学领域不会受到人工智能的影响,其中人工智能辅助临床决策发挥着尤为重要的作用。人工智能方法正在医学工作流程管理以及治疗成功或治疗结果预测方面得到确立。人工智能系统已经能够为基于影像的诊断和患者管理提供支持,但无法做出关键决策。借助人工智能可以更合理地评估相应的预防或治疗措施,尽管目前涵盖的疾病数量过少,尚无法创建用于常规临床使用的强大系统。广泛使用人工智能系统的前提是进行适当培训,使医生能够决定何时可以依赖计算机辅助决策。

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Artificial Intelligence in Obstetrics and Gynaecology: Is This the Way Forward?人工智能在妇产科中的应用:这是未来的发展方向吗?
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[Artificial intelligence in medicine-the wrong track or promise of cure?].[医学中的人工智能——走错路还是治愈的希望?]
HNO. 2019 May;67(5):343-349. doi: 10.1007/s00106-019-0665-z.
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Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis.基于深度学习的、使用小型临床图像数据集开发的计算机辅助分类器在皮肤肿瘤诊断方面超越了经过董事会认证的皮肤科医生。
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Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images.2016 年国际皮肤成像协作国际研讨会生物医学成像挑战赛的结果:比较计算机算法和皮肤科医生对基于皮肤镜图像的黑色素瘤诊断的准确性。
J Am Acad Dermatol. 2018 Feb;78(2):270-277.e1. doi: 10.1016/j.jaad.2017.08.016. Epub 2017 Sep 29.