Devices used for diagnosing disease are often large, expensive, and require operation by trained professionals, which can result in delayed diagnosis and missed opportunities for timely treatment. However, wearable devices are being recognized as a new approach to overcoming these difficulties, as they are small, affordable, and easy to use. Recent advancements in wearable technology have made monitoring information possible from the surface of organs like the skin and eyes, enabling accurate diagnosis of the user's internal status. In this review, we categorize the body's organs into external (e.g., eyes, oral cavity, neck, and skin) and internal (e.g., heart, brain, lung, stomach, and bladder) organ systems and introduce recent developments in the materials and designs of wearable electronics, including electrochemical and electrophysiological sensors applied to each organ system. Further, we explore recent innovations in wearable electronics for monitoring of deep internal organs, such as the heart, brain, and nervous system, using ultrasound, electrical impedance tomography, and temporal interference stimulation. The review also addresses the current challenges in wearable technology and explores future directions to enhance the effectiveness and applicability of these devices in medical diagnostics. This paper establishes a framework for correlating the design and functionality of wearable electronics with the physiological characteristics and requirements of various organ systems.

Microplastics (MPs) are present not only in the environment but also in drinking water, food, and consumer products. These MPs being toxic, carcinogenic, endocrine disrupting, and genetic risk creators cause several diseases. Despite various approaches, the development of onsite applicable, facile, and quick MP detection methods is still challenging. Here, 3D‐plasmonic gold nanopocket (3D‐PGNP) nanoarchitecture is formed on a paper substrate for simultaneous MP filtration and detection. The paper‐based 3D‐PGNP is integrated with a syringe filter device, and then, MP‐containing solutions are injected through the syringe. Subsequent detection of the MPs using the surface‐enhanced Raman scattering (SERS) successfully identifies the MPs without pretreatment. The interface and volumetric hotspot generation of 3D‐PGNP around the captured MPs significantly improves the sensitivity, which is confirmed by finite‐difference time‐domain simulation. Then, the SERS mapping images obtained from a portable Raman spectrometer are transformed into digital signals via machine learning (ML) technique to identify and quantify the MP distribution. The developed SERS‐ML‐based MP detection method is applied for mixture MPs and for real matrix samples, demonstrating that the method provides improved accuracy. This system is expected to be used for various MPs detection and for environmentally hazardous substances, such as bacteria, viruses, and fungi.