人工智能与数据挖掘评估肺癌风险：挑战与机遇 - Suppr | 超能文献

Artificial Intelligence and Data Mining to Assess Lung Cancer Risk: Challenges and Opportunities.

作者信息

Pinsky Paul

机构信息

National Cancer Institute, Bethesda, Maryland (P.P.).

出版信息

Ann Intern Med. 2020 Nov 3;173(9):760-761. doi: 10.7326/M20-5673. Epub 2020 Sep 1.

DOI:10.7326/M20-5673

Abstract

摘要

文献AI研究员

20分钟写一篇综述，助力文献阅读效率提升50倍。

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

文档翻译

学术文献翻译模型，支持多种主流文档格式。

相似文献

1

Artificial Intelligence and Data Mining to Assess Lung Cancer Risk: Challenges and Opportunities.人工智能与数据挖掘评估肺癌风险：挑战与机遇

Ann Intern Med. 2020 Nov 3;173(9):760-761. doi: 10.7326/M20-5673. Epub 2020 Sep 1.

2

Artificial intelligence is improving the detection of lung cancer.人工智能正在提高肺癌的检测水平。

Nature. 2020 Nov;587(7834):S20-S22. doi: 10.1038/d41586-020-03157-9.

3

Artificial Intelligence-Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach.基于人工智能的非影像电子病历预测肺癌风险：深度学习方法。

J Med Internet Res. 2021 Aug 3;23(8):e26256. doi: 10.2196/26256.

4

[Performance of Deep-learning-based Artificial Intelligence on Detection of Pulmonary Nodules in Chest CT].基于深度学习的人工智能在胸部CT肺结节检测中的性能

Zhongguo Fei Ai Za Zhi. 2019 Jun 20;22(6):336-340. doi: 10.3779/j.issn.1009-3419.2019.06.02.

5

Artificial intelligence applications for thoracic imaging.人工智能在胸部成像中的应用。

Eur J Radiol. 2020 Feb;123:108774. doi: 10.1016/j.ejrad.2019.108774. Epub 2019 Dec 11.

6

[Pathological diagnosis of lung cancer based on deep transfer learning].基于深度迁移学习的肺癌病理诊断

Zhonghua Bing Li Xue Za Zhi. 2020 Nov 8;49(11):1120-1125. doi: 10.3760/cma.j.cn112151-20200615-00471.

7

Pulmonary anatomy, pathology and treatment: What will be the artificial intelligence and deep-learning role on them?肺部解剖学、病理学与治疗：人工智能和深度学习在其中将发挥什么作用？

Lung Cancer. 2021 Sep;159:181-182. doi: 10.1016/j.lungcan.2021.05.012. Epub 2021 May 11.

8

How Artificial Intelligence Will Change Medicine.人工智能将如何改变医学。

Nature. 2019 Dec;576(7787):S48. doi: 10.1038/d41586-019-03845-1.

9

Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades' development course and future prospect.从经典方法到深度学习辅助决策支持的肺部结节诊断演进：三十年的发展历程和未来展望。

J Cancer Res Clin Oncol. 2020 Jan;146(1):153-185. doi: 10.1007/s00432-019-03098-5. Epub 2019 Nov 30.

10

[Clinical Application of Artificial Intelligence Recognition Technology  in the Diagnosis of Stage T1 Lung Cancer].人工智能识别技术在T1期肺癌诊断中的临床应用

Zhongguo Fei Ai Za Zhi. 2019 May 20;22(5):319-323. doi: 10.3779/j.issn.1009-3419.2019.05.09.

引用本文的文献

1

Differentiation of early-stage tumors from benign lesions manifesting as pure ground-glass nodule: a clinical prediction study based on AI-derived quantitative parameters.早期肿瘤与表现为纯磨玻璃结节的良性病变的鉴别：基于人工智能衍生定量参数的临床预测研究

Front Oncol. 2025 May 19;15:1573735. doi: 10.3389/fonc.2025.1573735. eCollection 2025.

2

Electronic Health Records and Machine Learning for Early Detection of Lung Cancer and Other Conditions: Thinking about the Path Ahead.电子健康记录与机器学习用于肺癌及其他疾病的早期检测：展望未来之路

Am J Respir Crit Care Med. 2021 Aug 15;204(4):389-390. doi: 10.1164/rccm.202104-1009ED.