A Comparative Study of Regulatory Guidelines, Digital Pharmacovigilance, and Software as a Medical Device across High- and Low-to-Middle-Income Countries

Research Article

Abstract

The rapid integration of artificial intelligence within pharmaceutical public health provides significant opportunities for optimizing therapeutic outcomes, yet it introduces profound regulatory, ethical, and systemic vulnerabilities. Deployment of AI can range from digital pharmacovigilance utilizing machine learning to scan electronic medical records and social media for adverse drug reactions and machine learning-driven drug discovery, where algorithms accelerate lead compound identification. Despite these technological strides, global governance remains structurally fragmented between high-income countries and low- and middle-income countries. High-income jurisdictions deploy mature, legally enforceable norms leveraging Software as a Medical Device classifications pioneered by the European Medicines Agency and the United States Food and Drug Administration. On the other hand, low- and middle-income countries encounter severe implementation barriers, primarily driven by the absence of specialized digital health sub-units within national pharmaceutical regulatory authorities, fragmented digital registries, and restricted technical capacity to audit predictive models. This disparity threatens to deepen global health inequalities, leaving developing healthcare systems susceptible to unvalidated therapeutic algorithms and biased diagnostic software. Mitigating these governance asymmetries requires a shift from aspirational ethical declarations to localized, risk-based legislative frameworks. Establishing dedicated digital health divisions within national medicines boards, standardizing local health data registries, and leveraging regional regulatory coalitions represent essential interventions to secure equitable, safe, and transparent algorithmic applications. Sustainable international cooperation must prioritize technical capacity transfer to prevent the colonization of digital health infrastructure and to ensure that algorithmic innovations serve diverse population demographics equitably.