Multidisciplinary considerations of fairness in medical AI: A scoping review

Yue Wang; Yaxin Song; Zhuo Ma; Xiaoxue Han

doi:10.1016/j.ijmedinf.2023.105175

Multidisciplinary considerations of fairness in medical AI: A scoping review

Int J Med Inform. 2023 Oct:178:105175. doi: 10.1016/j.ijmedinf.2023.105175. Epub 2023 Aug 8.

Authors

Yue Wang¹, Yaxin Song², Zhuo Ma³, Xiaoxue Han⁴

Affiliations

¹ School of Law, Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an, Shaanxi, 710049, PR China. Electronic address: wangyue2011@xjtu.edu.cn.
² School of Law, Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an, Shaanxi, 710049, PR China. Electronic address: yaxin0118@stu.xjtu.edu.cn.
³ School of Law, Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an, Shaanxi, 710049, PR China. Electronic address: mz00727@126.com.
⁴ Xi'an Jiaotong University Library, No.28, Xianning West Road, Xi'an, Shaanxi, 710049, PR China. Electronic address: hanxiaoxue011@xjtu.edu.cn.

PMID: 37595374
DOI: 10.1016/j.ijmedinf.2023.105175

Abstract

Introduction: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Algorithm; Artificial Intelligence; Bias; Data; Fairness; Medical AI.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Artificial Intelligence*
Consensus
Data Collection
Humans
Machine Learning