Background: Modern biomedical research is undergoing a fundamental transformation driven by an exponential increase in data complexity and the integration of artificial intelligence and cloud computing into biomedical and public health infrastructure. While cloud computing offers a pivotal solution to the limitations of on-premise infrastructure, the transition is hindered by a complex set of domain-specific challenges. This systematic review maps the current landscape of cloud adoption, identifies systemic barriers to wider usage, and outlines practical requirements for successful cloud migration. Methods: We conducted a comprehensive systematic search of articles published between 2010 and early 2026 across PubMed and Google Scholar. To capture both clinical and technical literature, the search was expanded to include major computer science and engineering databases, namely IEEE Xplore and Web of Science. We identified 68 primary studies evaluating 50 unique applications and subjected these to a formal assessment using a 5-point informatics quality appraisal (IQA) scale to determine the maturity of evidence. Results: Our analysis revealed a highly mature ecosystem, with 96% of identified applications reaching production-ready or validated implementation stages (IQA scores = 3-5). These applications were categorized into three functional types: task-specific tools, integrated platforms, and large-scale archives. Despite a projected market growth exceeding 17%, however, a significant readiness gap persists. Innovation remains concentrated in North America and is characterized by high data-egress costs, regulatory misalignments, and friction associated with technical interoperability. In contrast, initiatives such as the District Health Information System-2 (DHIS-2) demonstrate the potential for cloud applications to drive global health equity in resource-limited settings. Conclusion: The cloud has successfully moved from being a conceptual framework to serving as the backbone of modern health research. Nevertheless, a considerable trust deficit coupled with persistent structural barriers related to costs, data sovereignty, and regulatory complexity continue to impede global collaboration. To fully realize the promise of democratizing research, future development must transition from performance-centric models to federated, context-aware architectures that prioritize accessibility, inclusivity and secure data sharing. From a policy and practice perspective, funding agencies, governments, and international research consortia should support the adoption of such architectures while promoting harmonized cross-border data governance frameworks and measures to reduce prohibitive cloud data-egress costs. These efforts are essential for enabling equitable participation in global health research ecosystems, particularly among institutions in low- and middle-income countries.
Mugonza et al. (2026) studied this question.