Microwaves are non-ionizing electromagnetic radiation with waves of electrical and magnetic energy transmitted at different frequencies. They are widely used in various industries, including the food industry, telecommunications, weather forecasting, and in the field of medicine. Microwave applications in medicine are relatively a new field of growing interest, with a significant trend in healthcare research and development. The first application of microwaves in medicine dates to the 1980s in the treatment of cancer via ablation therapy; since then, their applications have been expanded. Significant advances have been made in reconstructing microwave data for imaging and sensing applications in the field of healthcare. Artificial intelligence (AI)-enabled microwave systems can be developed to augment healthcare, including clinical decision making, guiding treatment, and increasing resource-efficient facilities. An overview of recent developments in several areas of microwave applications in medicine, namely microwave imaging, dielectric spectroscopy for tissue classification, molecular diagnostics, telemetry, biohazard waste management, diagnostic pathology, biomedical sensor design, drug delivery, ablation treatment, and radiometry, are summarized. In this contribution, we outline the current literature regarding microwave applications and trends across the medical industry and how it sets a platform for creating AI-based microwave solutions for future advancements from both clinical and technical aspects to enhance patient care.

As the usage of wireless technology grows, it demands more complex architectures and conformal geometries, making the manufacturing of radio frequency (RF) systems challenging and expensive. The incorporation of emerging alternative manufacturing technologies, like additive manufacturing (AM), could consequently be a unique and cost-effective solution for flexible RF and microwave circuits and devices. This work presents manufacturing methodologies of 3D-printed conformal microstrip antennas made of a commercially available conductive filament, Electrifi, as the conductive trace on a commercially available nonconductive filament, NinjaFlex, as the substrate using the fused filament fabrication (FFF) method of AM technology. Additionally, a complete high frequency characterization of the prototyped antenna was studied and presented here through a comparative analysis between full-wave simulation and measurements in a fully calibrated anechoic chamber. The prototyped antenna measures 65.55 × 55.55 × 1.2 mm³ in size and the measured results show that the 3D-printed Electrifi based patch antenna achieved very good impedance matching at a resonant frequency of 2.4 GHz and a maximum antenna gain of −2.78 dBi. Finally, conformality performances of the developed antenna were demonstrated by placing the antenna prototype on five different cylindrical curved surfaces for possible implementation in flexible electronics, smart communications, and radar applications.

Evolution of mobile broadband is ensured by adopting a unified and more capable radio interface (RI). For ubiquitous connectivity among a wide variety of wireless applications, the RI enables the adoption of an adaptive bandwidth with high spectrum flexibility. To this end, the modern-day communication system needs to cater to extremely high bandwidth, starting from below 1 GHz to 100 GHz, based on different deployments. This instigates the creation of a platform called the Internet of Everything (IoE), which is based on the concept of all-round connectivity involving humans to different objects or things via sensors. In simple words, IoE is the intelligent connection of people, processes, data, and things. To enable seamless connectivity, IoE resorts to low-cost, compact, and flexible broadband antennas, RFID-based sensors, wearable electromagnetic (EM) structures, circuits, wireless body area networks (WBAN), and the integration of these complex elements and systems. IoE needs to ensure broader information dissemination via simultaneous transmission of data to multiple users through separate beams and to that end, it takes advantage of metamaterials. The precise geometry and arrangement of metamaterials enable smart properties capable of manipulating EM waves and essentially enable the metamaterial devices to be controlled independently to achieve desirable EM characteristics, such as the direction of propagation and reflection. This review paper presents a comprehensive study on next-generation EM devices and techniques, such as antennas and circuits for wearable and sub 6 GHz 5G applications, WBAN, wireless power transfer (WPT), the direction of arrival (DoA) of propagating waves, RFID based sensors for biomedical and healthcare applications, new techniques of metamaterials as well as transformation optics (TO) and its applications in designing complex media and arbitrary geometry conformal antennas and optical devices that will enable future IoE applications.

The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (${\mathsf{\epsilon }}_{\mathrm{r}}$) and conductivity ($\mathsf{\sigma }$), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.

The human voice is an important medium of communication and expression of feelings or thoughts. Disruption in the regulatory systems of the human voice can be analyzed and used as a diagnostic tool, labeling voice as a potential “biomarker”. Conversational artificial intelligence is at the core of voice-powered technologies, enabling intelligent interactions between machines. Due to its richness and availability, voice can be leveraged for predictive analytics and enhanced healthcare insights. Utilizing this idea, we reviewed artificial intelligence (AI) models that have executed vocal analysis and their outcomes. Recordings undergo extraction of useful vocal features to be analyzed by neural networks and machine learning models. Studies reveal machine learning models to be superior to spectral analysis in dynamically combining the huge amount of data of vocal features. Clinical applications of a vocal biomarker exist in neurological diseases such as Parkinson’s, Alzheimer’s, psychological disorders, DM, CHF, CAD, aspiration, GERD, and pulmonary diseases, including COVID-19. The primary ethical challenge when incorporating voice as a diagnostic tool is that of privacy and security. To eliminate this, encryption methods exist to convert patient-identifiable vocal data into a more secure, private nature. Advancements in AI have expanded the capabilities and future potential of voice as a digital health solution.