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

Research identifies significant philosophical and practical challenges hindering the readiness for Artificial Intelligence (AI) implementation in public health disease surveillance within Low- and Middle-Income Countries (LMICs). While AI's efficacy in outbreak prediction and early detection is recognized, its successful deployment is hampered by fragmented health information systems, digital inequality, governance issues, and institutional incapability. Existing research has largely overlooked these foundational conditions for AI adoption.

Why it matters

The successful integration of AI into public health systems, particularly in vulnerable regions, is crucial for global health security and effective disease management. Addressing the identified systemic challenges is paramount to leveraging AI's full potential and ensuring equitable access to advanced surveillance capabilities worldwide.

Key insights

• AI is recognized as a credible means to improve disease surveillance through early detection, epidemic prediction, and evidence-based decision-making.
• Challenges to AI implementation in LMICs include fragmented health information systems, digital inequality, governance problems, and institutional incapability.
• Prior research has predominantly focused on AI efficacy and accuracy in predicting outbreaks, rather than the broader conditions necessary for deployment.
• The study employs a qualitative problem-discovery research design using thematic analysis and a philosophical approach to identify underlying issues.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.39513