Background Cancer mortality-to-incidence ratios in Africa remain significantly higher than in high-income settings, driven by late diagnosis, limited specialist capacity, limited access to information and fragmented surveillance systems. Digital oncology frameworks are increasingly recognised as critical enablers of cancer control and management; however, their architectural characteristics have not been systematically synthesised to inform scalable platform development and deployment. This paper systematically maps digital oncology frameworks across Africa, characterises their dominant architectural patterns, digital maturity and AI integration levels, and derives evidence-informed design recommendations for future platforms with explicit attention to how architectural choices shape health data equity across diverse African health system contexts. Methods A scoping review was conducted following Arksey and O’Malley and Joanna Briggs Institute guidelines, with PRISMA-ScR reporting. Searches were performed across PubMed, ScienceDirect, Web of Science, IEEE Xplore, and African Journals Online. Frameworks were classified into six categories: population-based cancer registries; hospital-based oncology information systems; tele-oncology platforms; mHealth frameworks; cancer information hubs; and genomic/precision oncology systems. Data extracted included architecture type, data flow, interoperability, digital maturity, and AI integration. Results and discussion Fifty-three frameworks were identified. Registries were predominantly centralised at Digital Maturity Level 2, with higher maturity achieved through national health system integration. Hospital oncology information systems revealed a trade-off between vendor-integrated high-performance platforms and more interoperable open-source alternatives. Tele-oncology adopted scalable hub-and-spoke architectures supporting specialist reach to underserved facilities. mHealth frameworks were largely unidirectional SMS systems, effective for community engagement but limited in clinical integration. Cancer information hubs ranged from centralised analytical repositories to DHIS2-based interoperable systems. Genomic frameworks operated as federated research networks with limited clinical translation. AI integration was limited across all categories, reflecting underlying data standardisation and architectural deficits. Critically, the structural fragmentation, interoperability gaps, and geographic concentration of higher-maturity systems in well-resourced facilities collectively constitute a health data equity deficit; systematically excluding lower-resourced populations and settings from the benefits of digitally enabled cancer care. Conclusion Digital oncology systems in Africa are architecturally diverse but structurally fragmented. Advancing equitable cancer care requires interoperability-first, nationally embedded, and AI-ready digital architectures capable of supporting scalable, longitudinal, and inclusive oncology data ecosystems across diverse African health system contexts.
William et al. (2026) studied this question.