Intelligent diagnostics: AI-augmented microfluidic devices for non-invasive and minimally invasive disease biomarker monitoring.

Decentralised point-of-care testing (POCT) is driving a paradigm shift towards non-invasive and minimally invasive continuous health monitoring. While alternative biofluids, such as saliva, sweat, tears, exhaled breath condensate, and interstitial fluid (ISF) offer a patient-centric window into systemic physiology, their clinical utility remains constrained by ultra-low biomarker concentrations, severe matrix complexity, high analytical noise, and ambiguous correlations with established blood reference ranges. This review examines how micro-total analysis systems (µTAS), augmented by advanced functional materials and artificial intelligence (AI), systematically overcome these analytical bottlenecks. We evaluate wearable, implantable, and handheld microfluidic architectures, focusing on how passive fluidic logic and on-chip pre-concentration matrices (e.g., cellulose nanofibers and hydrogels) minimise biofouling and sample attenuation. Furthermore, we assess next-generation transduction modalities, spanning low-power electrochemical arrays, optical colorimetric assays, and vibrational spectroscopy (IR and Raman), amplified by functional porous nanomaterials, including MXenes, metal-organic frameworks (MOFs), and covalent organic frameworks (COFs). Crucially, we detail the pivotal shift from conventional linear calibration models to machine learning (ML)-driven multivariate deconvolution. We review the deployment of computational frameworks-including supervised/unsupervised learning, convolutional neural networks (CNNs), and long short-term memory (LSTM) networks for direct microfluidic signal denoising, dynamic sensor calibration, drift compensation, and multiplexed pattern decoding. Finally, we address the technical, ethical, and regulatory hurdles to clinical translation, highlighting emerging frontiers such as Edge AI and federated learning. Synergistically unifying precision bio-interface fluidics with intelligent data pipelines could help move microfluidic POCT platforms from qualitative screening tools towards quantitative, predictive diagnostics, although most reported systems remain at the prototype or pilot stage and still require prospective clinical validation.