Determinants of collaboration dynamics and challenges in North-South data-driven health research in the African context: an epistemic injustice perspective

Introduction Collaborations between Global North and South institutions are increasingly important for advancing artificial intelligence (AI) and data science in healthcare, offering opportunities to address context-specific challenges and reduce inequities. However, these partnerships often face persistent issues related to sustainability, equity, and long-term impact. Drawing on concepts of epistemic injustice and structural imbalances between partners, this study examines the factors shaping the effectiveness of North–South data-driven health collaborations, with particular attention to which factors exert the strongest statistically significant influence on collaboration effectiveness. Methods The study conducted a cross-sectional survey of participants from the Data Science Initiative for Africa (DS-I Africa) network, covering the entire continent of Africa, and their partners from the Global North, and applies structural equation modeling to assess key determinants of collaboration outcomes. Results The analysis focuses on governance and leadership, human capacity, and project and institutional practices. Human capacity is the strongest positive predictor of collaboration effectiveness (β = 0.467, p = 0.008). Project and institutional practices show a significant negative association with collaboration effectiveness (β = −0.464, p = 0.016). Governance and leadership also demonstrate a significant effect on collaboration outcomes. Discussion The findings demonstrate that sustainable and equitable North–South collaborations depend on the alignment of governance, human capacity, and institutional practices. Moreover, addressing epistemic injustice requires system-level changes that promote shared leadership, ethical data governance, and strengthening human capacity as a reciprocal and inclusive process central to achieving balanced knowledge production and long-term collaboration impact.