• 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

Ensuring machine learning for healthcare works for all.

作者信息

McCoy Liam G, Banja John D, Ghassemi Marzyeh, Celi Leo Anthony

机构信息

Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada

Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.

出版信息

BMJ Health Care Inform. 2020 Nov;27(3). doi: 10.1136/bmjhci-2020-100237.

DOI:10.1136/bmjhci-2020-100237
PMID:33234535
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7689076/
Abstract
摘要

相似文献

1
Ensuring machine learning for healthcare works for all.确保适用于医疗保健领域的机器学习能为所有人服务。
BMJ Health Care Inform. 2020 Nov;27(3). doi: 10.1136/bmjhci-2020-100237.
2
The reproducibility issues that haunt health-care AI.困扰医疗保健人工智能的可重复性问题。
Nature. 2023 Jan;613(7943):402-403. doi: 10.1038/d41586-023-00023-2.
3
Equity in essence: a call for operationalising fairness in machine learning for healthcare.公平的本质：呼吁在医疗保健机器学习中实现公平性操作化。
BMJ Health Care Inform. 2021 Apr;28(1). doi: 10.1136/bmjhci-2020-100289.
4
Improving healthcare delivery through patient informatics and quality centric data.通过患者信息学和以质量为中心的数据改善医疗服务提供。
J Digit Imaging. 2011 Apr;24(2):177-8. doi: 10.1007/s10278-011-9363-4.
5
Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach.利用从全州范围的健康信息交换中提取的数据识别需要高级抑郁症护理的患者：一种机器学习方法。
J Med Internet Res. 2019 Jul 22;21(7):e13809. doi: 10.2196/13809.
6
Consumer health informatics and interactive visual learning tools for health.消费者健康信息学与健康领域的交互式视觉学习工具。
Int J Electron Healthc. 2010;5(4):414-24. doi: 10.1504/IJEH.2010.036211.
7
Social media based surveillance systems for healthcare using machine learning: A systematic review.基于社交媒体的机器学习医疗保健监测系统：一项系统综述。
J Biomed Inform. 2020 Aug;108:103500. doi: 10.1016/j.jbi.2020.103500. Epub 2020 Jul 2.
8
Digital health at fifteen: more human (more needed).数字健康十五年：更有人文关怀（更有必要）。
BMC Med. 2019 Mar 18;17(1):62. doi: 10.1186/s12916-019-1302-0.
9
Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper.美国医疗保健系统中自适应 CDS 安全有效使用的建议：AMIA 立场文件。
J Am Med Inform Assoc. 2021 Mar 18;28(4):677-684. doi: 10.1093/jamia/ocaa319.
10
Machine Learning in Health Care.医疗保健中的机器学习。
J Perianesth Nurs. 2018 Oct;33(5):763-766. doi: 10.1016/j.jopan.2018.07.001.

引用本文的文献

1
Economic Evaluations and Equity in the Use of Artificial Intelligence in Imaging Examinations for Medical Diagnosis in People With Dermatological, Neurological, and Pulmonary Diseases: Systematic Review.皮肤病、神经疾病和肺部疾病患者医学诊断影像检查中人工智能应用的经济评估与公平性：系统评价
Interact J Med Res. 2025 Aug 13;14:e56240. doi: 10.2196/56240.
2
Advancements and challenges in high-sensitivity cardiac troponin assays: diagnostic, pathophysiological, and clinical perspectives.高敏心肌肌钙蛋白检测的进展与挑战：诊断、病理生理及临床视角
Clin Chem Lab Med. 2025 Feb 7;63(7):1260-1278. doi: 10.1515/cclm-2024-1090. Print 2025 Jun 26.
3
Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: a narrative review.理解和培训大语言模型和人工智能在医疗实践中的影响：叙事性综述。
BMC Med Educ. 2024 Oct 7;24(1):1096. doi: 10.1186/s12909-024-06048-z.
4
Federated-learning-based prognosis assessment model for acute pulmonary thromboembolism.基于联邦学习的急性肺血栓栓塞症预后评估模型。
BMC Med Inform Decis Mak. 2024 May 27;24(1):141. doi: 10.1186/s12911-024-02543-x.
5
Editorial: Artificial intelligence and machine learning in pediatric surgery.社论：儿科手术中的人工智能与机器学习
Front Pediatr. 2024 Apr 9;12:1404600. doi: 10.3389/fped.2024.1404600. eCollection 2024.
6
Real-World Evidence Studies in Oncology Therapeutics: Hope or Hype?肿瘤治疗中的真实世界证据研究：希望还是炒作？
Indian J Surg Oncol. 2023 Dec;14(4):829-835. doi: 10.1007/s13193-023-01784-y. Epub 2023 Jun 21.
7
Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers.人工智能教育：基于证据的医学方法，适用于消费者、翻译人员和开发者。
Cell Rep Med. 2023 Oct 17;4(10):101230. doi: 10.1016/j.xcrm.2023.101230.
8
Streams, rivers and data lakes: an introduction to understanding modern electronic healthcare records.溪流、河流和数据湖：了解现代电子医疗记录的入门介绍。
Clin Med (Lond). 2023 Jul;23(4):409. doi: 10.7861/clinmed.2022-0325.
9
Decentralized federated learning through proxy model sharing.通过代理模型共享的去中心化联邦学习。
Nat Commun. 2023 May 22;14(1):2899. doi: 10.1038/s41467-023-38569-4.
10
Best practices in the real-world data life cycle.真实世界数据生命周期中的最佳实践。
PLOS Digit Health. 2022 Jan 18;1(1):e0000003. doi: 10.1371/journal.pdig.0000003. eCollection 2022 Jan.

本文引用的文献

1
CheXclusion: Fairness gaps in deep chest X-ray classifiers.CheXclusion：深度学习胸部 X 射线分类器中的公平性差距。
Pac Symp Biocomput. 2021;26:232-243.
2
Hidden in Plain Sight - Reconsidering the Use of Race Correction in Clinical Algorithms.隐匿于众目睽睽之下——重新审视临床算法中种族校正的应用
N Engl J Med. 2020 Aug 27;383(9):874-882. doi: 10.1056/NEJMms2004740. Epub 2020 Jun 17.
3
How Medical Education Is Missing the Bull's-eye.医学教育如何未击中靶心。
N Engl J Med. 2020 Jun 25;382(26):2489-2491. doi: 10.1056/NEJMp1915891.
4
Structural racism in precision medicine: leaving no one behind.精准医学中的结构性种族主义：不落下任何人。
BMC Med Ethics. 2020 Feb 19;21(1):17. doi: 10.1186/s12910-020-0457-8.
5
Treating health disparities with artificial intelligence.用人工智能解决健康差异问题。
Nat Med. 2020 Jan;26(1):16-17. doi: 10.1038/s41591-019-0649-2.
6
Dissecting racial bias in an algorithm used to manage the health of populations.剖析用于管理人群健康的算法中的种族偏见。
Science. 2019 Oct 25;366(6464):447-453. doi: 10.1126/science.aax2342.
7
Assessment of Accuracy of an Artificial Intelligence Algorithm to Detect Melanoma in Images of Skin Lesions.评估人工智能算法在皮肤损伤图像中检测黑色素瘤的准确性。
JAMA Netw Open. 2019 Oct 2;2(10):e1913436. doi: 10.1001/jamanetworkopen.2019.13436.
8
Do no harm: a roadmap for responsible machine learning for health care.《医疗保健负责任机器学习的路线图：不造成伤害》。
Nat Med. 2019 Sep;25(9):1337-1340. doi: 10.1038/s41591-019-0548-6. Epub 2019 Aug 19.
9
A comprehensive review of randomized clinical trials in three medical journals reveals 396 medical reversals.对三家医学期刊的随机临床试验进行综合回顾，发现有 396 项医学研究结果发生了逆转。
Elife. 2019 Jun 11;8:e45183. doi: 10.7554/eLife.45183.
10
Women in clinical trials: a review of policy development and health equity in the Canadian context.临床试验中的女性：加拿大政策制定和卫生公平性的回顾。
Int J Equity Health. 2019 Apr 15;18(1):56. doi: 10.1186/s12939-019-0954-x.