Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review. - Ting Wang; Elham Emami; Dana Jafarpour; Raymond Tolentino; Genevieve Gore; Samira Abbasgholizadeh Rahimi (PLOS Digital Health)
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.