Socioeconomic impact of artificial intelligence–driven point-of-care testing devices for liquid biopsy in the OncoCheck system

Abstract

Cancer disparities in low- and middle-income countries (LMICs) persist because of socioeconomic inequalities and limited access to screening infrastructure, which requires equitable diagnostic solutions. As researchers, we need to develop interventions which mirror successful strategies from high-income countries (HICs) to address mortality inequalities. Routine cancer diagnosis functions as a fundamental element of effective management yet remains unavailable to numerous populations in LMICs. This review proposes the conceptual "OncoCheck" model, which combines the terms Oncology "Onco" and Screening "Check" as an integrated approach to early cancer detection. It provides a theoretically sound practical approach that combines liquid biopsy with point-of-care testing (POCT) and artificial intelligence (AI) to achieve high-sensitivity diagnostics in resource-limited settings without requiring advanced infrastructure. The review advocates OncoCheck as a promising and practical cancer screening solution which shows potential to increase accessibility and decrease costs while improving survival rates through early detection. Moving beyond technical specifications, the manuscript assesses its socioeconomic impact, showing reduced medical costs and improved treatment outcomes. The paper describes its implementation framework together with a validation strategy and performance benchmarks. The analysis further focuses on the implementation barriers like algorithmic bias mitigation, infrastructure limitations, and ethical AI deployment. The OncoCheck system delivers equitable cancer care by implementing a hospital-at-home model which functions with real-world health systems.

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