AI to promote sex/gender in evidence synthesis
Sex and gender are often overlooked in evidence synthesis. To facilitate and guide their consideration, the project will evaluate AI tools and develop a framework through scoping reviews, surveys, agreement studies, and participatory approaches.
Project description
Sex/gender analysis in evidence synthesis remains insufficient, particularly in systematic reviews and clinical guidelines. While AI can potentially accelerate evidence synthesis, its uncritical application may introduce sex/gender bias. There is a need to evaluate the performance of AI-driven methods for accelerating the consideration of sex/gender in the synthesis of research evidence. Digital health interventions for patients with cancer will serve as the use case for applying the methods.
Research aim
The project aims to accelerate sex/gender analysis in evidence synthesis. We will assess how clinical guidelines address sex/gender and health equity, map AI tools for systematic reviews and evaluate their potential to enhance sex/gender analysis. Based on the findings, we will develop an AI framework to support faster and more inclusive evidence synthesis, providing practical guidance on incorporating sex/gender considerations for reviewers, guideline developers, editors and AI tool creators.
Purpose
The project addresses critical gaps in sex/gender analysis in evidence synthesis, advancing equity in healthcare. It supports evidence-based policymaking, encourages inclusive clinical guidelines for healthcare professions, advances AI applications and supports more equitable, personalised care for patients. Implementation involves incorporating findings into evidence synthesis processes, tools, educational programmes and policies prioritising sex and gender inclusivity.
Original title
Artificial Intelligence (AI) to Expedite Sex/Gender Analysis in Evidence Synthesis: a use case of Digital Health Interventions for Cancer Patients