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

Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

981
The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...
981
Data Validation01:03

Data Validation

7.1K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
7.1K
Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

2.9K
A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
2.9K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.4K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.4K
Healthcare Agencies II01:17

Healthcare Agencies II

1.1K
There are various healthcare agencies in the United States—some of which are managed by religious institutions and others by different government branches.
Parish nursing is a growing specialty nursing profession that focuses on holistic healthcare, health promotion, and illness prevention. It blends professional nursing practice with a health ministry, focusing on health and healing within the context of a Christian community. Parish nurses serve as health educators, referral sources,...
1.1K
Healthcare Agencies I01:18

Healthcare Agencies I

1.3K
Healthcare agencies provide healthcare services to people. In the United States, voluntary agencies are often non-profit centers sponsored by donations, grants, or fundraisers. One such organization is Meals on Wheels, which provides meals to the elderly and homebound. The American Heart Association and the American Lung Association are other non-profit community organizations. Doctors and nurses are frequently active members of these organizations, which offer health checks and educational...
1.3K

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

Artificial intelligence in dermatology: Clinical promise and environmental impact.

The Journal of investigative dermatology·2026
Same author

Evaluating the digital health technology landscape in sub-Saharan Africa and its implications for cardiovascular health.

NPJ cardiovascular health·2026
Same author

Session Introduction: AI and Machine Learning in Clinical Medicine Bridging or Separating Model Intelligence and Human Expertise.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same author

Holistic evaluation of large language models for medical tasks with MedHELM.

Nature medicine·2026
Same author

Policy brief: AI-first Medicaid: how CMS can build a smarter safety net with Precision Benefits.

NPJ digital medicine·2025
Same author

Reply to "When do large language models cross the line: "reasoning" red teaming in healthcare".

NPJ digital medicine·2025

相关实验视频

Updated: Feb 28, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.3K

使用大型语言模型来审计模型医疗保健偏见.

Zara N Ansari1, Aaron Fanous2, Jesutofunmi A Omiye3

  • 1Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, U.S.A., zansari6@stanford.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 可以检测人工智能系统中的偏差. 更小,更具成本效益的LLM可以成为精确的偏差检测器,特别是在使用高级提示技术时,如思维线程.

更多相关视频

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

1.7K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.1K

相关实验视频

Last Updated: Feb 28, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.3K
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

1.7K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.1K

科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 医疗保健信息学 医疗保健信息学

背景情况:

  • 大型语言模型 (LLM) 在医疗保健方面表现有前途,但表现出人口偏见.
  • 由于大数据量,手动偏差审计是不切实际的.
  • 法律法规可能会对其他模型进行偏差审计,但它们的有效性各不相同.

研究的目的:

  • 评估LLM大小和提示策略如何影响偏差检测.
  • 使用医疗保健数据集,比较不同LLM的偏差检测性能.
  • 确定成本效益高的LLM解决方案,用于人工智能的偏见审计.

主要方法:

  • 使用了斯坦福医疗保健红色团队数据集,其中包含提示,输出和偏见标签.
  • 已经测试了GPT-3.5-turbo,GPT-4o,lama3.3和o1-mini的偏差检测能力.
  • 采用提示技术,包括思维线程,以评估它们对偏见检测的影响.

主要成果:

  • 像o1-mini这样的较小型号实现了比GPT-4o更高的精度和F1得分.
  • 大型模型中的自我批评特征并没有显著改善偏见检测.
  • 思考线索促使所有测试模型的偏差检测得到大幅增强.

结论:

  • 较小的LLM可以在偏差检测方面有效且具有成本效益,特别是当精度是关键时.
  • 提示技术对于改进基于LLM的偏见审计至关重要.
  • 选择用于偏差检测的LLM应与特定的指标优先级保持一致.