Human–machine interfacing (HMI) has emerged as a critical technology in healthcare, robotics, and wearable electronics, with hydrogels offering unique advantages as multifunctional materials that seamlessly connect biological systems with electronic devices. This review provides a detailed examination of recent advancements in hydrogel design, focusing on their properties and potential applications in HMI. We explore the key characteristics such as biocompatibility, mechanical flexibility, and responsiveness, which are essential for effective and long-term integration with biological tissues. Additionally, we highlight innovations in conductive hydrogels, hybrid and composite materials, and fabrication techniques such as 3D/4D printing, which allow for the customization of hydrogel properties to meet the demands of specific HMI applications. Further, we discuss the diverse classes of polymers that contribute to hydrogel conductivity, including conducting, natural, synthetic, and hybrid polymers, emphasizing their role in enhancing electrical performance and mechanical adaptability. In addition to material design, we examine the regulatory landscape governing hydrogel-based biointerfaces for HMI applications, addressing the key considerations for clinical translation and commercialization. An analysis of the patent landscape provides insights into emerging trends and innovations shaping the future of hydrogel technologies in human–machine interactions. The review also covers a range of applications, including wearable electronics, neural interfaces, soft robotics, and haptic systems, where hydrogels play a transformative role in enhancing human–machine interactions. Thereafter, the review addresses the challenges hydrogels face in HMI applications, including issues related to stability, biocompatibility, and scalability, while offering future perspectives on the continued evolution of hydrogel-based systems for HMI technologies.

1. Hypertension remains the leading modifiable driver of cardiovascular and renal morbidity worldwide. Population-based analyses indicate that more than 1.2 billion adults aged 30–79 years were liv...

Background/Objectives: Automated inspection is valuable when visual and process signals map to critical quality attributes (CQAs). This narrative review evaluates computer vision and complementary process analytical technology (PAT) for surface integrity, coating structure, chemical distribution, internal defects, and release-relevant quality. Methods: We synthesize selected literature on solid-dosage inspection, multimodal sensing, machine learning, and hybrid digital twins using a targeted narrative approach. Results: Visible imaging supports rapid surface inspection; optical coherence tomography and terahertz methods resolve coating and subsurface structure; Raman, near-infrared, and hyperspectral methods add chemical or spatial evidence. Task-specific pharmaceutical studies report promising performance, but rare defects, data leakage, domain shift, calibration, missing inputs, throughput, and lifecycle drift constrain generalization. CQA-driven fusion requires product-specific validation, calibrated uncertainty, human review, change control, and traceable data. Conclusions: Hybrid digital twins can support prospective scenario analysis and process understanding, but current evidence does not support autonomous batch release. Inspection outputs remain in-process evidence unless validated within a control strategy or surrogate relationship for the specified release attribute.