From Lab Innovation to Community Impact: Scaling Affordable Diagnostics for Early Disease Detection-2026
Author
Olorunnisola Adeola Oluwasegun
Abstract
Despite significant advances in diagnostic science, a persistent gap remains between laboratory innovation and real-world deployment, particularly in low-resourceand community-based settings where early disease detection is most critical. At a systems level, this gap is driven by high costs, fragmented supply chains, limitedtechnical capacity, and poor integration of diagnostics into primary care pathways. As a result, many preventable and manageable conditions including infectiousdiseases and non-communicable disorders are still diagnosed at advanced stages, increasing treatment costs and mortality rates. This study proposes a scalablemodel for translating laboratory-developed diagnostics into affordable, community-deployable solutions by integrating AI-enabled point-of-care (POC) technologies with decentralized healthcare delivery systems. The framework emphasizes three core pillars: cost-efficient device design, AI-driven diagnosticinterpretation, and digitally connected distribution networks. By embedding machine learning algorithms into portable diagnostic platforms, the model enablesrapid screening and triage by minimally trained health workers, reducing reliance on centralized laboratories. Furthermore, mobile integration supports real-timedata transmission, epidemiological tracking, and remote clinical oversight. The findings demonstrate that scaling affordable diagnostics through this integratedapproach can significantly improve early detection rates, reduce diagnostic turnaround time, and enhance health system responsiveness in underserved populations.
Keywords
AI-enabled point-of-care diagnostics, early disease detection, decentralized healthcare systems, diagnostic scalability, community health deployment, digital health integration
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References
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