Key points are not available for this paper at this time.
INTRODUCTION: Artificial intelligence (AI) has the potential to improve efficiency and quality of care in healthcare settings. The lack of consideration for equity, diversity and inclusion (EDI) in the lifecycle of AI within healthcare settings may intensify social and health inequities, potentially causing harm to under-represented populations. This article describes the protocol for a scoping review of the literature relating to integration of EDI in the AI interventions within healthcare setting. The objective of the review is to evaluate what has been done on integrating EDI concepts, principles and practices in the lifecycles of AI interventions within healthcare settings. It also aims to explore which EDI concepts, principles and practices have been integrated into the design, development and implementation of AI in healthcare settings. METHOD AND ANALYSIS: The scoping review will be guided by the six-step methodological framework developed by Arksey and O'Malley supplemented by Levac et al, and Joanna Briggs Institute methodological framework for scoping reviews. Relevant literature will be identified by searching seven electronic databases in engineering/computer science and healthcare, and searching the reference lists and citations of studies that meet the inclusion criteria. Studies on AI in any healthcare and geographical settings, that have considered aspects of EDI, published in English and French between 2005 and present will be considered. Two reviewers will independently screen titles, abstracts and full-text articles according to inclusion criteria. We will conduct a thematic analysis and use a narrative description to describe the work. Any disagreements will be resolved through discussion with the third reviewer. Extracted data will be summarised and analysed to address aims of the scoping review. Reporting will follow the Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews. The study began in April 2022 and is expected to end in September 2023. The database initial searches resulted in 5,745 records when piloted in April 2022. ETHICS AND DISSEMINATION: Ethical approval is not required. The study will map the available literature on EDI concepts, principles and practices in AI interventions within healthcare settings, highlight the significance of this context, and offer insights into the best practices for incorporating EDI into AI-based solutions in healthcare settings. The results will be disseminated through open-access peer-reviewed publications, conference presentations, social media and 2-day workshops with relevant stakeholders.
Nyariro et al. (Fri,) studied this question.