偏见算法:医疗保健中的人工智能如何加剧种族和种族差异 - 范围审查
Syed Ali Hussain1, Mary Bresnahan2, Jie Zhuang3
1College of Communication, University of Sharjah, Sharjah, UAE.
Ethnicity & health
|November 3, 2024
概括
人工智能健康算法 (AIHA) 是有前途的,但由于种族和种族偏见,可能会加剧健康不平等. 解决偏见需要利益相关者的参与和强有力的治理,以实现公平的AI部署.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 健康 公平 研究 健康 公平 研究
背景情况:
- 人工智能健康算法 (AIHA) 在诊断和治疗优化方面取得了进展.
- 然而,AIHA对患者隐私,数据安全和机构透明度提出了重大挑战.
- 现有证据表明,AIHA可以延续和加剧健康不平等.
研究的目的:
- 检查AIHA内部的种族和种族偏见.
- 确定利益相关者在AIHA监督中的作用.
- 评估AIHA对健康平等的影响.
主要方法:
- 根据PRISMA-ScR指南进行了范围审查.
- 搜索跨越了2020-2024年,使用与健康算法中的种族和种族偏见相关的术语.
- 分析了23个来源的最终样本,以编制缓解策略,确定利益相关者的角色,并审查治理计划.
主要成果:
- AIHA可以超越人类的诊断精度和准确性.
- 在AIHA中,种族和民族偏见被确定为一个重大问题.
- AIHA有可能延续现有的健康不平等.
结论:
- 对算法偏差的缓解策略需要编译和评估.
- 利益相关者的参与对于有效的AIHA监督至关重要.
- 强有力的治理和管理计划对于确保AIHA促进而不是阻碍全球健康公平至关重要.
更多相关视频
03:14Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
505
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
14.4K
相关概念视频
Issues And Trends In Healthcare Delivery System
5.6K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.6K
Current Trends in Nursing II
1.2K
Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
1.2K
Ethical Issues
856
Nurses are essential in patient care, upholding the ethical principles of their profession and effectively navigating ethical dilemmas. Neglecting ethical issues can lead to inadequate patient care, compromised therapeutic relationships, and moral distress among healthcare workers.
Ethical Concerns in Healthcare:
Ethical Concerns in Healthcare:
856
Stereotypes, Prejudice, and Discrimination
90.0K
Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
90.0K
Methods of Documentation VI: Case Management Model
559
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...
For example, a patient with a chronic...
559
Bias in Epidemiological Studies
161
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:
161
