# Combined Use of Microwave Sensing Technologies and Artificial Intelligence for Biomedical Monitoring and Imaging

## TL;DR

This paper reviews how combining microwave sensing and AI can improve biomedical monitoring and imaging, highlighting recent advancements and challenges.

## Contribution

The paper provides a comprehensive review of the integration of microwave sensing and AI in biomedical applications, emphasizing recent innovations and challenges.

## Key findings

- Microwave sensing combined with AI shows promise for compact and affordable medical devices.
- Recent works demonstrate effective AI integration in processing microwave-derived biomedical data.
- Key challenges include technical, algorithmic, and translational barriers to widespread adoption.

## Abstract

Microwave sensing technology is rapidly advancing and increasingly finding its way into biomedical applications, promising significant improvements for medical care. Concurrently, the rise of artificial intelligence (AI) is enabling significant enhancements in the biomedical domain. Close scrutiny of the recent literature reveals intense activity in both fields, with particularly impactful outcomes deriving from the combined use of advanced microwave techniques and AI for biomedical monitoring. In this review, an up-to-date compilation, from the perspective of the authors, of the most significant works published on these topics in recent years is given, focusing on their integration and current challenges. With the objective of analyzing the current landscape, we survey and compare state-of-the-art biosensors and imaging systems at all healthcare levels, from outpatient contexts to specialized medical equipment and laboratory analysis tools. We also delve into the relevant applications of AI in medicine for processing microwave-derived data. As our core focus, we analyze the synergistic integration of AI in the design of microwave devices and the processing of the acquired data, which have shown notable performances, opening new avenues for compact, affordable, and multi-functional medical devices. We conclude by synthesizing the prevailing technical, algorithmic, and translational challenges that must be addressed to realize this potential.