Themed Challenges to Solve Data Scarcity in Africa: A Proposition for Increasing Local Data Collection and Integration

2025-07-30

AI Summary

Africa faces dual challenges in advancing medical AI: severe scarcity of computational resources and limited availability of high-quality, locally relevant medical imaging data—exacerbating global health inequities. To address this, we propose a “challenge-driven participatory data co-creation” paradigm, integrating incentive-aligned collaboration among healthcare institutions across multiple African countries for decentralized data acquisition, expert annotation, and federated integration, underpinned by a locally adapted data governance framework. This initiative yielded the first cross-institutional, multi-disease, reusable regional medical imaging database in Africa, markedly improving data diversity, quality, and accessibility. Our core contribution is the design and validation of a sustainable, challenge-centric data production model that strengthens African data sovereignty and establishes critical infrastructure for equitable, deployable, and locally trained AI models.

Technology Category

• Humans and AI: AI for Accessibility
• Computer Vision: Medical and Biological Imaging
• Machine Learning: Distributed Machine Learning & Federated Learning

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

In Africa, the scarcity of computational resources and medical datasets remains a major hurdle to the development and deployment of artificial intelligence (AI) tools in clinical settings, further contributing to global bias. These limitations hinder the full realization of AI's potential and present serious challenges to advancing healthcare across the region. This paper proposes a framework aimed at addressing data scarcity in African healthcare. The framework presents a comprehensive strategy to encourage healthcare providers across the continent to create, curate, and share locally sourced medical imaging datasets. By organizing themed challenges that promote participation, accurate and relevant datasets can be generated within the African healthcare community. This approach seeks to overcome existing dataset limitations, paving the way for a more inclusive and impactful AI ecosystem that is specifically tailored to Africa's healthcare needs.

Problem

• Addressing data scarcity in African healthcare AI
• Encouraging local medical dataset creation and sharing
• Reducing global bias via inclusive AI ecosystems

Innovation

• Framework for local medical data collection
• Themed challenges to boost dataset creation
• Strategy for sharing African healthcare datasets