Artificial intelligence in clinical physiology: How to improve learning agility

Dmitry V. Shutov; Шутов Дмитрий Валериевич; Dmitry V. Shutov; Dariya E. Sharova; Шарова Дарья Евгеньевна; Dariya E. Sharova; Liya R. Abuladze; Абуладзе Лия Руслановна; Liya R. Abuladze; Dmitrii V. Drozdov; Дроздов Дмитрий Владимирович; Dmitrii V. Drozdov

doi:10.17816/DD123559

临床生理学中的人工智能系统：如何使其训练有效？

作者: Shutov D.V.¹, Sharova D.E.¹, Abuladze L.R.¹, Drozdov D.V.²
隶属关系:
1. Moscow Center for Diagnostics and Telemedicine
2. National Medical Research Center of Cardiology
期: 卷 4, 编号 1 (2023)
页面: 81-88
栏目: 致编辑的一封信
URL: https://journals.rcsi.science/DD/article/view/146878
DOI: https://doi.org/10.17816/DD123559
ID: 146878

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详细
全文:
作者简介
参考
补充文件
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详细

临床生理学是关于在病理前和病理情况下身体内发生的生理过程变化的作用和性质的一个医学科学分支，它要求对患病和健康器官的功能进行完整、全面、多边的研究，从而允许评估身体的补偿能力。

使用人工智能技术创造的软件和各种硬件系统更积极地被用于医学的各个领域，包括临床生理学。医疗数据集的出现、不断提高的计算能力、云服务的发展以及证明这种智能解决方案的有效性和前景的众多出版物都有助于这个过程。

虽然医学数据集的形成方法大体相似，但临床生理学有一系列关键特征和显著差异。遵守我们提出的数据集形成规则将有可能使临床生理学中的人工智能系统接受有效的训练并得到实际应用。

生效的俄罗斯联邦GOST R 59921.9-2022标准被纳入“临床医学中的人工智能系统”这套标准，这种标准对临床生理学中使用的人工智能系统的数据分析算法和测试方法提出额外要求。新标准的一个重要特点是其拟度量类型（附有一套强制性的示范数据）。

俄罗斯是世界上最早开始制定拟度量标准的国家之一，人工智能方面的15项行业标准（其中两项是与医学方面有关的）将于今年生效。

关键词

数据集, 心电描记法, 临床生理学, 注释, 心电图自动分析

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作者简介

Dmitry V. Shutov

Moscow Center for Diagnostics and Telemedicine

编辑信件的主要联系方式.
Email: ShutovDV@zdrav.mos.ru
ORCID iD: 0000-0003-1836-3689
SPIN 代码: 9381-2456

MD, Dr. Sci. (Med.)

俄罗斯联邦, Moscow

Dariya E. Sharova

Moscow Center for Diagnostics and Telemedicine

Email: ShutovDV@zdrav.mos.ru
ORCID iD: 0000-0001-5792-3912
SPIN 代码: 1811-7595
俄罗斯联邦, Moscow

Liya R. Abuladze

Moscow Center for Diagnostics and Telemedicine

Email: AbuladzeLR@zdrav.mos.ru
ORCID iD: 0000-0001-6745-1672
SPIN 代码: 8640-9989

Junior Research Associate

俄罗斯联邦, Moscow

Dmitrii V. Drozdov

National Medical Research Center of Cardiology

Email: cardioexp@gmail.com
ORCID iD: 0000-0001-7374-3604
SPIN 代码: 2279-9657

MD, Cand. Sci. (Med.)

俄罗斯联邦, Moscow

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