Integrating health equity in artificial intelligence for public health in Canada: a rapid narrative review.

Abstract

Introduction: The application of artificial intelligence (AI) in public health is rapidly evolving, offering promising advancements in various public health settings across Canada. AI has the potential to enhance the effectiveness, precision, decision-making, and scalability of public health initiatives. However, to leverage AI in public health without exacerbating inequities, health equity considerations must be addressed. This rapid narrative review aims to synthesize health equity considerations related to AI application in public health.

Methods: A rapid narrative review methodology was used to identify and synthesize literature on health equity considerations for AI application in public health. After conducting title/abstract and full-text screening of articles, and consensus decision on study inclusion, the data extraction process proceeded using an extraction template. Data synthesis included the identification of challenges and opportunities for strengthening health equity in AI application for public health.

Results: The review included 54 peer-review articles and grey literature sources. Several health equity considerations for applying AI in public health were identified, including gaps in AI epistemology, algorithmic bias, accessibility of AI technologies, ethical and privacy concerns, unrepresentative training datasets, lack of transparency and interpretability of AI models, and challenges in scaling technical skills.

Conclusion: While AI has the potential to advance public health in Canada, addressing equity is critical to preventing inequities. Opportunities to strengthen health equity in AI include implementing diverse AI frameworks, ensuring human oversight, using advanced modeling techniques to mitigate biases, fostering intersectoral collaboration for equitable AI development, and standardizing ethical and privacy guidelines to enhance AI governance.