Towards grey-zone reliability in CA19-9 nanobiosensing: an AI-integrated molecular interface design-to-decision framework.

Mildly elevated CA19-9 levels (typically 37-300 U mL-1) define a diagnostic grey zone that fuels both patient anxiety and overdiagnosis, yet many nanobiosensing works continue to prioritise ever-lower detection limits over clinical reliability in this ambiguous interval. In this review, we argue for a use-case-specific shift from sensitivity-only reporting to reliability-centred translational nanobiosensing. An integrated "grey-zone reliability metrology" framework that unifies antifouling interface engineering, high-affinity molecular recognition, signal amplification, and AI-assisted interpretation as a single co-dependent system is proposed. Through interference-aware denoising, longitudinal trajectory modelling, and patient-specific baseline inference, borderline CA19-9 signals can be qualified and contextualised for clinical interpretation. The proposed design-to-decision logic may be adaptable to other grey-zone biomarkers, such as cardiac troponins and circulating tumour DNA, offering a cautious transferable framework for next-generation diagnostics that balance sensitivity with decision reliability. The framework is intended to qualify analytical uncertainty in an already measurable interval rather than to resolve the intrinsic disease-specific limitations of CA19-9 itself.