aceMachine learning in point-of-care testing: innovations, challenges, and opportunities
The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (POCT). The COVID-19 pandemic has accelerated the shift from centralized laboratory testing but also catalyzed the development of next-generation POCT platforms that leverage ML to enhance the accuracy, sensitivity, and overall efficiency of point-of-care sensors. Thi…
ML integration into point-of-care platforms improves diagnostic accuracy, sensitivity, and efficiency and can expand decentralized testing access.
ML-enhanced point-of-care testing faces regulatory hurdles, reliability questions, and privacy concerns that limit widespread clinical adoption.
Widespread clinical adoption is constrained by unresolved regulatory, reliability, and privacy issues that the perspective identifies as needing to be overcome.
Evidence
- Peer-reviewedNature Communications2025-04-02
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Truvace Impact Record TRV-2026-0532, v1: “Machine learning in point-of-care testing: innovations, challenges, and opportunities.” Truvace, 2026-07-24. /record/TRV-2026-0532 (accessed at citation time). sha256 2b979a2ec8b5bfaf…
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