Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Dopaminergic and perfusion modulation in non-fluent variant primary progressive aphasia following iTBS: A case report.

Asian journal of psychiatry·2026
Same author

Distinct Genetic Alterations Drive Cushing Disease Versus Silent Corticotroph Adenomas.

The Journal of clinical endocrinology and metabolism·2026
Same author

Delusional Pseudotranssexualism Associated With Exogenous Estrogen in Schizoaffective Disorder: A Case Report.

Journal of clinical psychopharmacology·2026
Same author

The role of alcohol metabolizing gene in patients with alcohol use disorder and heroin use disorder in the Taiwan Han Chinese population.

Progress in neuro-psychopharmacology & biological psychiatry·2026
Same author

Brexpiprazole-Induced Pisa Syndrome in a Patient With Dementia With Lewy Bodies.

American journal of therapeutics·2026
Same author

Complete Acid-Based Hydrolysis Assay for Carbohydrate Quantification in Seaweed: A Species-Specific Optimized Approach.

Methods in molecular biology (Clifton, N.J.)·2026

相关实验视频

Updated: Jan 8, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

747

对比式学习提高了病理学中的公平性 人工智能系统 人工智能系统

Shih-Yen Lin1, Pei-Chen Tsai2, Fang-Yi Su2

  • 1Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.

Cell reports. Medicine
|December 17, 2025
PubMed
概括

人工智能在癌症诊断中的偏见被FAIR-Path减少,这是一个新的框架. 这种AI公平性工具显著降低了跨不同患者群体的病理学评估中的绩效差距.

关键词:
在这里,我们可以看到AIAIAI.算法偏差是一种算法偏差.人工智能的人工智能是人工智能.偏见缓解和减轻偏见的方法癌症的诊断 癌症的诊断相反的学习学习学习.深度学习是一种深度学习.公平的公平的公平.病理学的病理学缺乏监督的学习学习.

更多相关视频

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.4K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.2K

相关实验视频

Last Updated: Jan 8, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

747
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.4K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.2K

科学领域:

  • 医疗人工智能 医疗人工智能
  • 计算病理学计算病理学
  • 健康 公平 卫生 公平

背景情况:

  • 人工智能驱动的病理系统显示出癌症诊断的前景.
  • 现有的人工智能模型通常会显示出对代表性不足的人群的偏见,原因是训练数据多样性有限.

研究的目的:

  • 引入对病理学的公平意识的人工智能审查 (FAIR-Path) 框架.
  • 在基于AI的病理评估系统中减轻人口偏见.
  • 解决人工智能驱动的医学诊断中的公平性挑战.

主要方法:

  • 利用对比式学习和弱监督机器学习.
  • 在20种癌症类型中进行了泛癌AI公平性分析.
  • 在15个独立队伍中进行了外部验证.

主要成果:

  • 在29.3%的跨种族,性别和年龄组的诊断任务中发现了显著的绩效差异.
  • 公平之路减轻了88.5%的发现差异.
  • 外部验证表明,绩效差距减少了91.1%.

结论:

  • 身体突变发病率的变化有助于人工智能性能差异.
  • FAIR-Path提供了一个强大的框架来缓解医疗AI中的偏见.
  • 这种方法促进了人工智能驱动的病理学和癌症诊断的公平性.