1196621519c63285fac-3c7f 20260216050958528 chitu:chitu chitkarauniversitypublications@chitkara.edu.in WEB-FORM Journal of Multidisciplinary Research in Healthcare JMRH 23938536 23938544 10.15415/jmrh https://jmrh.chitkara.edu.in/ 01 29 2026 01 29 2026 12 1 10.15415/jmrh.2025.121 https://jmrh.chitkara.edu.in/2025/volume-12-and-issue-1/ Digital AI-Based Sensing Technologies in Cancer Care: A PATHS Framework for Early Detection and Personalized Diagnosis Department of MLT, University Institute of Allied Health Science, Chandigarh University, Mohali-140413, Punjab, India. M Vijayasimha . https://orcid.org/0000-0003-2038-7006 Logesh Babu https://orcid.org/0009-0000-6316-3542 University Institute of Allied Health Science, Chandigarh University, Mohali-140413, Punjab, India. Maitri Chakraborty https://orcid.org/0009-0007-1685-2186 Calcutta Institute of Science and Management, Tollygunge, Kolkata, West Bengal 700040, India. 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. 01 29 2026 01 29 2026 50 55 10.15415/jmrh.2025.121006 https://jmrh.chitkara.edu.in/2025/digital-ai-based-sensing-technologies-in-cancer-care-a-paths-framework-for-early-detection-and-personalized-diagnosis/