School of Law, Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an, Shaanxi, 710049, PR China.
Xi'an Jiaotong University Library, No.28, Xianning West Road, Xi'an, Shaanxi, 710049, PR China.
Int J Med Inform. 2023 Oct;178:105175. doi: 10.1016/j.ijmedinf.2023.105175. Epub 2023 Aug 8.
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.
人工智能（AI）技术近年来得到了迅猛发展。由于医疗 AI 直接关系到人类的生命和健康，其公平性备受关注。本综述旨在从计算机科学、医学和社会科学（包括法律和伦理）的角度分析现有的医疗 AI 公平性研究文献。综述的目的是检查对公平性的理解的异同，探索影响因素，并调查在英语和中文文献中实施医疗 AI 公平性的潜在措施。
本研究采用范围综述方法，选择了以下数据库：Web of Science、MEDLINE、PubMed、OVID、CNKI、WANFANG Data 等，以 2023 年 2 月为截止日期，检索医疗 AI 公平性问题的相关文献。使用了各种关键词，如“人工智能”、“机器学习”、“医学”、“算法”、“公平”、“决策”和“偏差”进行搜索。收集的数据进行了图表制作、综合，并进行了描述性和主题分析。
在回顾了 468 篇英文论文和 356 篇中文论文后，最终分析纳入了 53 篇英文论文和 42 篇中文论文。我们的研究结果表明，三个不同学科在核心问题的研究上都存在显著差异。数据是影响医疗 AI 公平性的基础，除了算法偏差和人为偏差之外。法律、伦理和技术措施都促进了医疗 AI 公平性的实施。
我们的综述表明，跨多学科视角一致认为数据公平性作为实现医疗 AI 公平性的基础至关重要。然而，医疗 AI 公平性的核心方面，如概念、影响因素和实施措施，存在很大差异。因此，未来的研究应该促进跨学科讨论，弥合不同领域之间的认知差距，增强医疗 AI 公平性的实际实施。
