Artificial Intelligence, Geospatial Analytics, and Healthcare Accessibility: Emerging Strategies for Inclusive Pharmaceutical Service Delivery

Abstract

The convergence of artificial intelligence (AI) and geospatial analytics represents a transformative paradigm in pharmaceutical service delivery and healthcare accessibility. This analytical review examines how AI-driven geospatial intelligence addresses systemic inequities in medication access, particularly in resource-constrained and geographically marginalized settings. This paper evaluates the methodological integration of machine learning algorithms with geographic information systems (GIS) to optimize pharmaceutical supply chains, predict accessibility gaps, and inform evidence-based policy interventions. The analysis reveals that hybrid AI-geospatial models demonstrate superior performance in identifying pharmacy deserts, with machine learning-based gravity models achieving 95% population coverage through strategic facility placement (Prabhune et al., 2024). However, critical challenges persist, including algorithmic bias, data heterogeneity, and the digital divide that threatens to exacerbate existing health inequities. The paper synthesizes emerging strategies for inclusive pharmaceutical service delivery, including drone-enabled last-mile distribution, predictive demand forecasting, and equity-centered spatial optimization frameworks. Findings indicate that successful implementation requires addressing data sovereignty concerns, establishing interoperability standards, and embedding equity considerations throughout the AI development lifecycle. This research contributes to the theoretical understanding of how computational intelligence can be leveraged to achieve universal health coverage while highlighting the imperative for context-specific, ethically grounded approaches that prioritize vulnerable populations in pharmaceutical service planning.