Wang Ting, Emami Elham, Jafarpour Dana, Tolentino Raymond, Gore Genevieve, Rahimi Samira Abbasgholizadeh
Department of Family Medicine, McGill University, Montreal, Quebec, Canada.
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, Canada.
PLOS Digit Health. 2025 Jul 14;4(7):e0000941. doi: 10.1371/journal.pdig.0000941. eCollection 2025 Jul.
The lack of Equity, Diversity, and Inclusion (EDI) principles in the lifecycle of Artificial Intelligence (AI) technologies in healthcare is a growing concern. Despite its importance, there is still a gap in understanding the initiatives undertaken to address this issue. This review aims to explore what and how EDI principles have been integrated into the design, development, and implementation of AI studies in healthcare. We followed the scoping review framework by Levac et al. and the Joanna Briggs Institute. A comprehensive search was conducted until April 29, 2022, across MEDLINE, Embase, PsycInfo, Scopus, and SCI-EXPANDED. Only research studies in which the integration of EDI in AI was the primary focus were included. Non-research articles were excluded. Two independent reviewers screened the abstracts and full texts, resolving disagreements by consensus or by consulting a third reviewer. To synthesize the findings, we conducted a thematic analysis and used a narrative description. We adhered to the PRISMA-ScR checklist for reporting scoping reviews. The search yielded 10,664 records, with 42 studies included. Most studies were conducted on the American population. Previous research has shown that AI models improve when socio-demographic factors such as gender and race are considered. Despite frameworks for EDI integration, no comprehensive approach systematically applies EDI principles in AI model development. Additionally, the integration of EDI into the AI implementation phase remains under-explored, and the representation of EDI within AI teams has been overlooked. This review reports on what and how EDI principles have been integrated into the design, development, and implementation of AI technologies in healthcare. We used a thorough search strategy and rigorous methodology, though we acknowledge limitations such as language and publication bias. A comprehensive framework is needed to ensure that EDI principles are considered throughout the AI lifecycle. Future research could focus on strategies to reduce algorithmic bias, assess the long-term impact of EDI integration, and explore policy implications to ensure that AI technologies are ethical, responsible, and beneficial for all.
医疗保健领域人工智能（AI）技术生命周期中缺乏公平、多样性和包容性（EDI）原则，这一问题日益受到关注。尽管其重要性，但在理解为解决该问题所采取的举措方面仍存在差距。本综述旨在探讨EDI原则在医疗保健领域人工智能研究的设计、开发和实施中是如何整合的，以及整合了哪些内容。我们遵循了Levac等人以及乔安娜·布里格斯研究所的范围综述框架。截至2022年4月29日，在MEDLINE、Embase、PsycInfo、Scopus和SCI-EXPANDED数据库中进行了全面检索。仅纳入了以EDI在AI中的整合为主要重点的研究。非研究性文章被排除。两名独立评审员筛选了摘要和全文，通过达成共识或咨询第三位评审员来解决分歧。为了综合研究结果，我们进行了主题分析并采用了叙述性描述。我们遵循PRISMA-ScR清单来报告范围综述。检索共得到10664条记录，其中42项研究被纳入。大多数研究是针对美国人群开展的。先前的研究表明，在考虑性别和种族等社会人口因素时，人工智能模型会有所改进。尽管有EDI整合框架，但没有一种全面的方法能在人工智能模型开发中系统地应用EDI原则。此外，EDI在人工智能实施阶段的整合仍未得到充分探索，并且人工智能团队中EDI的代表性也被忽视了。本综述报告了EDI原则在医疗保健领域人工智能技术的设计、开发和实施中是如何整合的，以及整合了哪些内容。我们采用了全面的检索策略和严谨的方法，不过我们承认存在语言和发表偏倚等局限性。需要一个全面的框架来确保在人工智能的整个生命周期中都考虑到EDI原则。未来的研究可以聚焦于减少算法偏差的策略、评估EDI整合的长期影响以及探索政策含义，以确保人工智能技术符合道德规范、负责任且对所有人都有益。
