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人工智能与甲状腺疾病管理:甲状腺功能检测的考虑因素。

Artificial intelligence and thyroid disease management: considerations for thyroid function tests.

机构信息

Department of Clinical Biochemistry, Cliniques Universitaires Saint-Luc, Université catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium.

Department of Biochemistry, Pant Institute of Postgraduate Medical Education & Research, Delhi, India.

出版信息

Biochem Med (Zagreb). 2022 Jun 15;32(2):020601. doi: 10.11613/BM.2022.020601.

DOI:10.11613/BM.2022.020601
PMID:35799984
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9195598/
Abstract

Artificial intelligence (AI) is transforming healthcare and offers new tools in clinical research, personalized medicine, and medical diagnostics. Thyroid function tests represent an important asset for physicians in the diagnosis and monitoring of pathologies. Artificial intelligence tools can clearly assist physicians and specialists in laboratory medicine to optimize test prescription, tests interpretation, decision making, process optimization, and assay design. Our article is reviewing several of these aspects. As thyroid AI models rely on large data sets, which often requires distributed learning from multi-center contributions, this article also briefly discusses this issue.

摘要

人工智能(AI)正在改变医疗保健领域,为临床研究、个性化医疗和医学诊断提供新工具。甲状腺功能测试是医生在诊断和监测疾病方面的重要工具。人工智能工具可以为医生和医学检验专家提供帮助,优化检验方案、检验解读、决策制定、流程优化和检测设计。本文综述了其中的几个方面。由于甲状腺 AI 模型依赖于大型数据集,这通常需要通过多中心贡献进行分布式学习,本文还简要讨论了这个问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e374/9195598/73b6519e54d5/bm-32-2-020601-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e374/9195598/73b6519e54d5/bm-32-2-020601-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e374/9195598/73b6519e54d5/bm-32-2-020601-f1.jpg

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Monoplex and multiplex immunoassays: approval, advancements, and alternatives.单重和多重免疫测定:批准、进展及替代方法
Comp Clin Path. 2022;31(2):333-345. doi: 10.1007/s00580-021-03302-4. Epub 2021 Nov 20.
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Systems biomarkers for papillary thyroid cancer prognosis and treatment through multi-omics networks.
人工智能将变革内分泌学领域:综述
Front Endocrinol (Lausanne). 2025 Jan 14;16:1513929. doi: 10.3389/fendo.2025.1513929. eCollection 2025.
4
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Biochem Med (Zagreb). 2025 Feb 15;35(1):010501. doi: 10.11613/BM.2025.010501. Epub 2024 Dec 15.
通过多组学网络用于甲状腺乳头状癌预后和治疗的系统生物标志物
Arch Biochem Biophys. 2022 Jan 15;715:109085. doi: 10.1016/j.abb.2021.109085. Epub 2021 Nov 17.
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Lessons Learned from the COVID-19 Pandemic: Emphasizing the Emerging Role and Perspectives from Artificial Intelligence, Mobile Health, and Digital Laboratory Medicine.从新冠疫情中吸取的教训:强调人工智能、移动健康和数字实验室医学的新兴作用及观点
EJIFCC. 2021 Jun 29;32(2):224-243. eCollection 2021 Jun.
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Surg Pathol Clin. 2021 Sep;14(3):493-506. doi: 10.1016/j.path.2021.05.011.
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