背景:近年来,人工智能(AI)技术得到了显着发展。医疗人工智能的公平性因其与人类生命和健康的直接关系而备受关注。这篇综述旨在从计算机科学的角度分析现有的关于医学人工智能公平性的研究文献,医学科学,和社会科学(包括法律和伦理学)。检讨的目的,是研究对公平的理解的异同,探索影响因素,并研究在英汉文献中实施医学人工智能公平性的潜在措施。
方法:本研究采用了范围审查方法,并选择了以下数据库:WebofScience,MEDLINE,Pubmed,OVID,CNKI,万方数据,等。,到2023年2月,医疗人工智能的公平性问题。搜索是使用各种关键字进行的,例如“人工智能,\"\"机器学习,\"\"医学,\"\"算法,\"\"公平,\"\"决策,“和”偏见。“收集的数据被绘制出来,合成,并进行描述性和主题分析。
结果:在审阅了468篇英文论文和356篇中文论文之后,53和42包括在最终分析中。我们的结果表明,三个不同的学科在核心问题的研究上都表现出显著的差异。除了算法偏差和人为偏差之外,数据是影响医疗AI公平性的基础。Legal,伦理,和技术措施都促进了医疗AI公平的实施。
结论:我们的综述表明,关于数据公平性作为跨多学科视角实现医学AI公平性的基础的重要性,达成了共识。然而,在概念、影响因素,以及医疗人工智能公平性的实施措施。因此,未来的研究应该促进跨学科的讨论,以弥合不同领域之间的认知差距,并加强医疗人工智能中公平性的实际实施。
Artificial Intelligence (AI) technology has been developed significantly in recent years. The fairness of medical AI is of great concern due to its direct relation to human life and health. This review aims to analyze the existing research literature on fairness in medical AI from the perspectives of computer science, medical science, and social science (including law and ethics). The objective of the review is to examine the similarities and differences in the understanding of fairness, explore influencing factors, and investigate potential measures to implement fairness in medical AI across English and Chinese literature.
This study employed a scoping review methodology and selected the following databases: Web of Science, MEDLINE, Pubmed, OVID, CNKI, WANFANG Data, etc., for the fairness issues in medical AI through February 2023. The search was conducted using various keywords such as \"artificial intelligence,\" \"machine learning,\" \"medical,\" \"algorithm,\" \"fairness,\" \"decision-making,\" and \"bias.\" The collected data were charted, synthesized, and subjected to descriptive and thematic analysis.
After reviewing 468 English papers and 356 Chinese papers, 53 and 42 were included in the final analysis. Our results show the three different disciplines all show significant differences in the research on the core issues. Data is the foundation that affects medical AI fairness in addition to algorithmic bias and human bias. Legal, ethical, and technological measures all promote the implementation of medical AI fairness.
Our review indicates a consensus regarding the importance of data fairness as the foundation for achieving fairness in medical AI across multidisciplinary perspectives. However, there are substantial discrepancies in core aspects such as the concept, influencing factors, and implementation measures of fairness in medical AI. Consequently, future research should facilitate interdisciplinary discussions to bridge the cognitive gaps between different fields and enhance the practical implementation of fairness in medical AI.