• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

神经科医生与人工智能：处于十字路口的巨头。

The Neurologist and Artificial Intelligence: Titans at Crossroads.

作者信息

Vishnu Venugopalan Y, Vinny Pulikottil Wilson

机构信息

Department of Neurology, All India Institute of Medical Sciences, New Delhi, India.

Department of Neurology, INHS Asvini, Mumbai, Maharashtra, India.

出版信息

Ann Indian Acad Neurol. 2019 Jul-Sep;22(3):264-266. doi: 10.4103/aian.AIAN_493_18.

DOI:10.4103/aian.AIAN_493_18
PMID:31359934
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6613413/
Abstract

Clinical judgment to reach final diagnosis has remained a challenge since time immemorial. The present times are witness to artificial intelligence (AI) and machine learning programs competing to outperform the seasoned physician in arriving at a differential diagnosis. We discuss here the possible roles of AI in neurology.

摘要

自古以来，通过临床判断得出最终诊断一直是一项挑战。如今，人工智能（AI）和机器学习程序竞相在进行鉴别诊断方面超越经验丰富的医生。我们在此讨论人工智能在神经病学中的可能作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2546/6613413/744d7c77584e/AIAN-22-264-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2546/6613413/744d7c77584e/AIAN-22-264-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2546/6613413/744d7c77584e/AIAN-22-264-g001.jpg

相似文献

1
The Neurologist and Artificial Intelligence: Titans at Crossroads.神经科医生与人工智能：处于十字路口的巨头。
Ann Indian Acad Neurol. 2019 Jul-Sep;22(3):264-266. doi: 10.4103/aian.AIAN_493_18.
2
Against the iDoctor: why artificial intelligence should not replace physician judgment.反对智能医生：为何人工智能不应取代医生的判断。
Theor Med Bioeth. 2018 Apr;39(2):91-110. doi: 10.1007/s11017-018-9442-3.
3
In Defence of Machine Learning: Debunking the Myths of Artificial Intelligence.为机器学习辩护：揭穿人工智能的神话
Eur J Psychol. 2018 Nov 30;14(4):734-747. doi: 10.5964/ejop.v14i4.1823. eCollection 2018 Nov.
4
Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?人工智能、机器学习、深度学习和认知计算：这些术语是什么意思，它们将如何影响医疗保健？
J Arthroplasty. 2018 Aug;33(8):2358-2361. doi: 10.1016/j.arth.2018.02.067. Epub 2018 Feb 27.
5
Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers.人工智能与临床决策支持系统在放射科医师和转诊医生中的应用。
J Am Coll Radiol. 2019 Sep;16(9 Pt B):1351-1356. doi: 10.1016/j.jacr.2019.06.010.
6
Artificial intelligence in neurosciences: A clinician's perspective.神经科学中的人工智能：临床医生的视角。
Neurol India. 2018 Jul-Aug;66(4):934-939. doi: 10.4103/0028-3886.236971.
7
How Bioethics Can Shape Artificial Intelligence and Machine Learning.生物伦理学如何塑造人工智能和机器学习
Hastings Cent Rep. 2018 Sep;48(5):10-13. doi: 10.1002/hast.895.
8
Artificial intelligence and deep learning in ophthalmology - present and future (Review).眼科中的人工智能与深度学习——现状与未来（综述）
Exp Ther Med. 2020 Oct;20(4):3469-3473. doi: 10.3892/etm.2020.9118. Epub 2020 Aug 12.
9
[Applications of machine learning in clinical decision support in the omic era].[机器学习在组学时代临床决策支持中的应用]
Yi Chuan. 2018 Sep 20;40(9):693-703. doi: 10.16288/j.yczz.18-139.
10
Artificial Intelligence-Based Traditional Chinese Medicine Assistive Diagnostic System: Validation Study.基于人工智能的中医辅助诊断系统：验证研究。
JMIR Med Inform. 2020 Jun 15;8(6):e17608. doi: 10.2196/17608.

引用本文的文献

1
Assessment of ChatGPT's performance on neurology written board examination questions.ChatGPT在神经病学笔试问题上的表现评估。
BMJ Neurol Open. 2023 Nov 2;5(2):e000530. doi: 10.1136/bmjno-2023-000530. eCollection 2023.
2
Addressing bias in big data and AI for health care: A call for open science.解决医疗保健领域大数据和人工智能中的偏见：呼吁开放科学。
Patterns (N Y). 2021 Oct 8;2(10):100347. doi: 10.1016/j.patter.2021.100347.
3
Artificial Intelligence shaping the future of neurology practice.人工智能塑造神经病学实践的未来。

本文引用的文献

1
Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study.深度学习算法在头部 CT 扫描中关键发现检测的应用：一项回顾性研究。
Lancet. 2018 Dec 1;392(10162):2388-2396. doi: 10.1016/S0140-6736(18)31645-3. Epub 2018 Oct 11.
2
Unintended Consequences of Machine Learning in Medicine.机器学习在医学领域的意外后果。
JAMA. 2017 Aug 8;318(6):517-518. doi: 10.1001/jama.2017.7797.
3
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
Med J Armed Forces India. 2021 Jul;77(3):276-282. doi: 10.1016/j.mjafi.2021.06.003. Epub 2021 Jul 1.
4
Will Artificial Intelligence Outperform the Clinical Neurologist in the Near Future? Yes.在不久的将来，人工智能会比临床神经科医生表现得更出色吗？会的。
Mov Disord Clin Pract. 2021 Apr 12;8(4):525-528. doi: 10.1002/mdc3.13202. eCollection 2021 May.
深度学习算法在视网膜眼底照片糖尿病视网膜病变检测中的开发与验证。
JAMA. 2016 Dec 13;316(22):2402-2410. doi: 10.1001/jama.2016.17216.
4
Automated quantification of cerebral edema following hemispheric infarction: Application of a machine-learning algorithm to evaluate CSF shifts on serial head CTs.半球梗死后脑水肿的自动定量分析：应用机器学习算法评估系列头颅CT上的脑脊液移位情况。
Neuroimage Clin. 2016 Sep 26;12:673-680. doi: 10.1016/j.nicl.2016.09.018. eCollection 2016.
5
Doctors beat computer programs in making diagnoses.医生在进行诊断方面比计算机程序更胜一筹。
BMJ. 2016 Oct 13;355:i5552. doi: 10.1136/bmj.i5552.
6
Comparison of Physician and Computer Diagnostic Accuracy.医生与计算机诊断准确性的比较。
JAMA Intern Med. 2016 Dec 1;176(12):1860-1861. doi: 10.1001/jamainternmed.2016.6001.
7
Outcomes and Complications After Endovascular Treatment of Brain Arteriovenous Malformations: A Prognostication Attempt Using Artificial Intelligence.脑动静脉畸形血管内治疗后的结局与并发症：一项使用人工智能的预后评估尝试
World Neurosurg. 2016 Dec;96:562-569.e1. doi: 10.1016/j.wneu.2016.09.086. Epub 2016 Sep 28.
8
Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.预测未来——大数据、机器学习与临床医学。
N Engl J Med. 2016 Sep 29;375(13):1216-9. doi: 10.1056/NEJMp1606181.
9
Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke.网络连接中断预示着中风后多个行为领域的损伤。
Proc Natl Acad Sci U S A. 2016 Jul 26;113(30):E4367-76. doi: 10.1073/pnas.1521083113. Epub 2016 Jul 11.
10
Predicting discharge mortality after acute ischemic stroke using balanced data.使用平衡数据预测急性缺血性卒中后的出院死亡率。
AMIA Annu Symp Proc. 2014 Nov 14;2014:1787-96. eCollection 2014.