Artificial intelligence in cancer diagnostics: Governance for equity in low- and middle-income countries

Abstract

Artificial intelligence (AI) is rapidly reshaping cancer diagnostics by enhancing accuracy, speed, and clinical decision support. However, its adoption in low- and middle-income countries (LMICs) raises critical governance challenges related to data protection, regulatory oversight, equity, and infrastructural readiness. This quantitative scoping review synthesizes evidence from 19 studies published between 2015 and 2024 to map the governance landscape surrounding AI-driven cancer diagnostics in LMICs. Following the PRISMA-ScR approach, the review identified key governance domains including data sovereignty, regulatory gaps, algorithmic transparency, infrastructural constraints, and risks of inequitable access. Results indicate that data governance challenges were the most frequently reported (n=14), followed by regulatory limitations (n=12) and workforce and infrastructural barriers (n=11). The findings highlight that while AI holds transformative potential for improving timely and accurate cancer diagnosis, its benefits cannot be realized without context-sensitive governance frameworks that ensure safety, transparency, accountability, and equity. The review proposes actionable policy pathways to support responsible AI integration across LMIC health systems.

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