Digital AI-Based Sensing Technologies in Cancer Care: A PATHS Framework for Early Detection and Personalized Diagnosis

Background: Recent work has mapped a wide range of biotechnological tools for early cancer detection, ranging from microfluidics and liquid biopsy to biosensors, organoids, breath-based diagnostics, and artificial intelligence (AI), with explicit attention to primary and resource-constrained healthcare settings. However, global experience with multi-cancer early detection (MCED) tests and liquid biopsy shows that technology alone does not guarantee earlier diagnosis or reduced mortality.

Purpose: This short communication proposes a pragmatic, pathway-first framework to complement tool-centric narratives and to help clinicians, policymakers, and innovators integrate emerging technologies into real-world primary healthcare systems, especially in low and middle-income countries (LMICs).

Methods: A focused narrative synthesis of recent literature from 2022 to 2025 was performed on early cancer detection, MCED, liquid biopsy, biosensors, breath-based diagnostics, radiomics, and AI in oncology, prioritizing peer-reviewed sources indexed in major biomedical databases. Insights from implementation science and equity-oriented cancer control in LMICs were integrated to co-develop a framework aligned with healthcare delivery and organization.

Results: Three key blind spots in purely tool-focused narratives were identified, namely limited integration of implementation science and health system readiness, insufficient attention to affordability, reimbursement, and financing, and lack of use-case clarity across screening, triage, diagnosis, and monitoring. To address these gaps, the PATHS framework is introduced: Performance for purpose, Access and affordability, Trust and ethics, Health system fit, and Sustainability. Its application is illustrated for wearable biosensors, breath-based tests, paper-based microfluidics, liquid biopsy, and radiomics or AI at different levels of care.

Conclusions: Biotechnological innovation for early cancer detection is now rich and diverse. The next step is to embed these tools into implementable, equity-sensitive pathways. Adopting a PATHS lens can help readers move from asking “which tool is most exciting?” to “which tool, in which pathway, for which population, delivers the greatest real-world benefit?”, particularly in primary healthcare and LMIC settings where the marginal gains from earlier detection are greatest.