Incorporating Equity, Diversity, and Inclusion into Cochrane Systematic Reviews with AI

Date & Time
Monday, September 4, 2023, 12:15 PM - 12:25 PM
Location Name
Session Type
Oral presentation
Transformation of Cochrane systems and processes for evidence synthesis
Oral session
Global health, equity, diversity and inclusion
Jahanfar S1
1Director of Cochrane Affiliate at Tufts School of Medicine, USA

Background: Cochrane systematic reviews are a valuable source of evidence-based information for healthcare decision-making. However, it is important to ensure that the analysis of these reviews considers equity, diversity, and inclusion (EDI) to represent diverse populations better and address disparities in health outcomes.
Objectives: This study aimed to assess the feasibility and effectiveness of using artificial intelligence (AI) to incorporate EDI considerations into the analysis of Cochrane systematic reviews.
Methods: A sample of Cochrane systematic reviews was analyzed using AI algorithms to identify EDI-related issues, such as disparities in study populations, missing data on diverse populations, and the under-representation of marginalized groups in the evidence base. The algorithms used machine learning techniques to analyze the data and provide insights into these disparities.
Results: The study found that AI algorithms effectively identified EDI-related issues in the analysis of Cochrane systematic reviews. The AI-powered analysis provided insights into disparities in study populations and the under-representation of marginalized groups in the evidence base. The algorithms also provided recommendations for improving EDI considerations in future studies and systematic reviews.
Conclusions: The study highlights the importance of considering EDI in analyzing Cochrane systematic reviews. Using AI to incorporate EDI considerations is a feasible and effective way to ensure that the information is representative of diverse populations and addresses disparities in health outcomes. The study demonstrates the potential of AI to support EDI in the analysis of evidence-based information and to improve the quality and representativeness of Cochrane systematic reviews. In this study, patient and public involvement was incorporated through consultation with stakeholders and subject matter experts to understand the barriers and facilitators to incorporating EDI considerations in the analysis of Cochrane systematic reviews. This engagement helped to ensure that the AI algorithms developed in this study effectively addressed the needs and perspectives of diverse populations and that the recommendations provided were relevant and meaningful to patients, the public, and healthcare consumers.