Translational Barriers and AI-Driven Challenges of Microfluidics-Enabled Wearables and Implantable Systems in Personalized Medicine.

Wearable and implantable microfluidic systems have progressed from laboratory prototypes toward translational clinical deployment, enabling continuous, minimally invasive sampling of dynamic biomarkers across sweat, saliva, tears, and interstitial fluid. However, existing reviews often address materials chemistry or device fabrication in isolation, obscuring the systemic path to clinical translation. This review establishes a cohesive, translation-focused linear trajectory starting from foundational functional biomaterials and advanced fabrication techniques, transitioning into a performance benchmarking of diagnostics-oriented systems and closed-loop theranostic platforms. Through these vectors, we systematically evaluate how microfluidic transport, multiplexed molecular analytics, and autonomous therapeutic integration collectively drive the shift from passive tracking to adaptive intervention. Beyond physical hardware, we decode the integration of artificial intelligence (AI) across three precise pathways: sensor self-calibration, multiplexed molecular decoding, and on-device autonomous decision-making. Finally, we critically examine the socio-technical barriers to clinical translation, with a focus on how biofluid data heterogeneity and population baseline disparities propagate algorithmic bias. We propose that robust hardware interfaces, standardized validation benchmarks, and alignment with emerging regulatory frameworks are prerequisites for achieving equitable, responsible digital health protection.