• 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

将机器学习和人工智能算法应用于医疗保健领域，以减少偏见并提高人口健康水平。

Targeting Machine Learning and Artificial Intelligence Algorithms in Health Care to Reduce Bias and Improve Population Health.

机构信息

Institute on Health Disparities, Equity, and the Exposome, Meharry Medical College.

School of Social Sciences, Humanities and Arts, University of California Merced.

出版信息

Milbank Q. 2024 Sep;102(3):577-604. doi: 10.1111/1468-0009.12712. Epub 2024 Aug 8.

DOI:10.1111/1468-0009.12712
PMID:39116187
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11576591/
Abstract

Policy Points Artificial intelligence (AI) is disruptively innovating health care and surpassing our ability to define its boundaries and roles in health care and regulate its application in legal and ethical ways. Significant progress has been made in governance in the United States and the European Union. It is incumbent on developers, end users, the public, providers, health care systems, and policymakers to collaboratively ensure that we adopt a national AI health strategy that realizes the Quintuple Aim; minimizes race-based medicine; prioritizes transparency, equity, and algorithmic vigilance; and integrates the patient and community voices throughout all aspects of AI development and deployment.

摘要

政策要点 人工智能（AI）正在颠覆医疗保健行业，并超越我们定义其在医疗保健中的边界和角色以及以合法和道德的方式监管其应用的能力。美国和欧盟在治理方面已经取得了重大进展。开发人员、最终用户、公众、提供者、医疗保健系统和政策制定者有责任共同确保我们采用国家人工智能健康战略，实现五重目标；最大限度地减少基于种族的医学；优先考虑透明度、公平性和算法警惕性；并在人工智能开发和部署的各个方面整合患者和社区的声音。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a11b/11576591/0ee7079bafa6/MILQ-102-577-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a11b/11576591/0ee7079bafa6/MILQ-102-577-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a11b/11576591/0ee7079bafa6/MILQ-102-577-g001.jpg

相似文献

1
Targeting Machine Learning and Artificial Intelligence Algorithms in Health Care to Reduce Bias and Improve Population Health.将机器学习和人工智能算法应用于医疗保健领域，以减少偏见并提高人口健康水平。
Milbank Q. 2024 Sep;102(3):577-604. doi: 10.1111/1468-0009.12712. Epub 2024 Aug 8.
2
Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives.医疗人工智能中算法偏差的实践、认知和规范影响：多学科专家观点的定性研究
J Med Ethics. 2025 May 21;51(6):420-428. doi: 10.1136/jme-2022-108850.
3
Ethical Implications of Artificial Intelligence in Vaccine Equity: Protocol for Exploring Vaccine Distribution Planning and Scheduling in Pandemics in Low- and Middle-Income Countries.人工智能在疫苗公平性方面的伦理影响：探索低收入和中等收入国家大流行期间疫苗分配规划与调度的方案
JMIR Res Protoc. 2025 Jul 9;14:e76634. doi: 10.2196/76634.
4
Ethics, Bias, and Governance in Artificial Intelligence for Hepatology: Toward Building a Safe and Fair Future.肝病学人工智能中的伦理、偏见与治理：迈向构建安全公正的未来
J Clin Exp Hepatol. 2025 Nov-Dec;15(6):102628. doi: 10.1016/j.jceh.2025.102628. Epub 2025 Jun 21.
5
Bridging Health Disparities in the Data-Driven World of Artificial Intelligence: A Narrative Review.在数据驱动的人工智能世界中弥合健康差距：一项叙述性综述
J Racial Ethn Health Disparities. 2024 Jul 2. doi: 10.1007/s40615-024-02057-2.
6
Trust, Trustworthiness, and the Future of Medical AI: Outcomes of an Interdisciplinary Expert Workshop.信任、可信度与医学人工智能的未来：跨学科专家研讨会成果
J Med Internet Res. 2025 Jun 2;27:e71236. doi: 10.2196/71236.
7
Exploring Gender Bias in AI for Personalized Medicine: Focus Group Study With Trans Community Members.探索人工智能在个性化医疗中的性别偏见：与跨性别群体成员进行的焦点小组研究。
J Med Internet Res. 2025 Jul 29;27:e72325. doi: 10.2196/72325.
8
Challenges and Opportunities for Data Sharing Related to Artificial Intelligence Tools in Health Care in Low- and Middle-Income Countries: Systematic Review and Case Study From Thailand.低收入和中等收入国家医疗保健领域与人工智能工具相关的数据共享面临的挑战与机遇：系统评价及来自泰国的案例研究
J Med Internet Res. 2025 Feb 4;27:e58338. doi: 10.2196/58338.
9
High-reward, high-risk technologies? An ethical and legal account of AI development in healthcare.高回报、高风险技术？医疗保健领域人工智能开发的伦理与法律剖析
BMC Med Ethics. 2025 Jan 15;26(1):4. doi: 10.1186/s12910-024-01158-1.
10
Does an App a Day Keep the Doctor Away? AI Symptom Checker Applications, Entrenched Bias, and Professional Responsibility.一天一个 App ，能否让医生远离我？人工智能症状检查应用程序、根深蒂固的偏见与专业责任。
J Med Internet Res. 2024 Jun 5;26:e50344. doi: 10.2196/50344.

引用本文的文献

1
The implementation of digital biomarkers in the diagnosis, treatment and monitoring of mood disorders: a narrative review.数字生物标志物在情绪障碍诊断、治疗和监测中的应用：一篇叙述性综述。
Front Digit Health. 2025 Jun 17;7:1595243. doi: 10.3389/fdgth.2025.1595243. eCollection 2025.
2
Fairness in Low Birthweight Predictive Models: Implications of Excluding Race/Ethnicity.低出生体重预测模型中的公平性：排除种族/族裔的影响
J Racial Ethn Health Disparities. 2025 Jan 29. doi: 10.1007/s40615-025-02296-x.
3
Feasibility of using an artificially intelligent chatbot to increase access to information and sexual and reproductive health services.

本文引用的文献

1
Accuracy of Online Artificial Intelligence Models in Primary Care Settings.在线人工智能模型在初级保健环境中的准确性。
Am J Prev Med. 2024 Jun;66(6):1054-1059. doi: 10.1016/j.amepre.2024.02.006. Epub 2024 Feb 12.
2
Centering health equity in large language model deployment.在大语言模型部署中以健康公平为核心。
PLOS Digit Health. 2023 Oct 24;2(10):e0000367. doi: 10.1371/journal.pdig.0000367. eCollection 2023 Oct.
3
Accuracy of a Generative Artificial Intelligence Model in a Complex Diagnostic Challenge.生成式人工智能模型在复杂诊断挑战中的准确性。
使用人工智能聊天机器人增加信息获取以及性与生殖健康服务可及性的可行性。
Digit Health. 2024 Dec 19;10:20552076241308994. doi: 10.1177/20552076241308994. eCollection 2024 Jan-Dec.
JAMA. 2023 Jul 3;330(1):78-80. doi: 10.1001/jama.2023.8288.
4
Ethical Implications of Artificial Intelligence in Population Health and the Public's Role in Its Governance: Perspectives From a Citizen and Expert Panel.人工智能在人口健康中的伦理影响及公众在其治理中的作用：来自公民和专家小组的观点。
J Med Internet Res. 2023 Apr 27;25:e44357. doi: 10.2196/44357.
5
Opportunities to Address Health Disparities in Performance-Based Accountability and Payment Programs.在基于绩效的问责制和支付计划中解决健康差异问题的机遇。
JAMA Health Forum. 2021 Jun 4;2(6):e211143. doi: 10.1001/jamahealthforum.2021.1143.
6
Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence.公众态度重视可解释性，但在人工智能中更重视准确性。
Nat Commun. 2022 Oct 3;13(1):5821. doi: 10.1038/s41467-022-33417-3.
7
Exclusion cycles: Reinforcing disparities in medicine.排除周期：加剧医学中的差异。
Science. 2022 Sep 9;377(6611):1158-1160. doi: 10.1126/science.abo2788. Epub 2022 Sep 8.
8
What Data Should Be Included in a Modern Public Health Data System.现代公共卫生数据系统应包含哪些数据
Big Data. 2022 Sep;10(S1):S9-S14. doi: 10.1089/big.2022.0205.
9
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.停止为高风险决策解释黑箱机器学习模型，转而使用可解释模型。
Nat Mach Intell. 2019 May;1(5):206-215. doi: 10.1038/s42256-019-0048-x. Epub 2019 May 13.
10
Framework for Integrating Equity Into Machine Learning Models: A Case Study.将公平性融入机器学习模型的框架：一个案例研究。
Chest. 2022 Jun;161(6):1621-1627. doi: 10.1016/j.chest.2022.02.001. Epub 2022 Feb 7.