From Knowledge to Legitimacy: A Philosophical Problem Discovery of AI Implementation Readiness in Public Health Disease Surveillance

2026-09-30

AI Summary

This study addresses the implementation challenges of AI-based disease surveillance in low- and middle-income countries arising from system fragmentation, digital inequality, and governance deficits. Employing a qualitative problem-identification design that integrates thematic analysis with philosophical inquiry, this work transcends the limitations of purely predictive models by pioneering a four-dimensional theoretical framework that conceptualizes AI deployment as a normative process. Encompassing epistemic, justice, ethical, and legitimacy dimensions, the proposed framework reconceptualizes implementation readiness pathways through a sociotechnical lens. Ultimately, this research provides foundational theoretical support for developing ethically compliant and institutionally sustainable AI surveillance systems in resource-constrained settings.

Abstract

Despite being an emerging credible means of using artificial intelligence (AI) for improving disease surveillance via early detection of outbreaks, epidemics prediction and evidence-based decision-making, there continue to be challenges in the use of AI tools in many low- and middle-income countries (LMICs) due to various factors including fragmented health information system, digital inequality, governance problems and institutional incapability. Most of the research conducted so far has concentrated on the efficacy and accuracy of AI in terms of predicting outbreaks, with little focus on other conditions necessary for its deployment. This study adopts a qualitative problem-discovery research design, integrating thematic analysis with philosophical analysis to examine the structural and normative barriers surrounding AI implementation in disease surveillance. The analysis identifies four interrelated dimensions of implementation readiness: epistemic adequacy, distributive justice, ethics of governance, and institutional legitimacy. These dimensions provide a framework for understanding how limitations in knowledge integration, unequal digital infrastructure, privacy and accountability concerns, and deficits in institutional and public trust can constrain the practical adoption of AI-enabled surveillance. Rather than proposing another predictive model, this study develops a theoretical framework that conceptualizes AI implementation in disease surveillance as simultaneously a socio-technical and normative process. The framework provides a foundation for subsequent empirical investigation and offers a structured perspective for designing more context-sensitive, ethically grounded, and institutionally sustainable AI-enabled disease surveillance systems in resource-constrained healthcare settings.

Problem

• Artificial Intelligence
• Disease Surveillance
• Implementation Readiness
• Low- and Middle-Income Countries
• Digital Inequality

Innovation

• Artificial Intelligence
• Disease Surveillance
• Implementation Readiness
• Philosophical Analysis
• Socio-technical Framework