A Review of Biosensors and Artificial Intelligence in Healthcare and their Clinical Significance

Abstract

In the past decade, a substantial increase in medical data from various sources, including wearable sensors, medical imaging, personal health records, and public health organizations, has propelled advancements in the medical sciences. The evolution of computational hardware, such as cloud computing, GPUs, FPGAs, and TPUs, has enabled the effective utilization of this vast amount of data. Consequently, sophisticated AI techniques have been developed to extract valuable insights from healthcare datasets. This article provides a comprehensive overview of recent developments in AI and biosensors within the medical and life sciences. The review highlights the role of machine learning in key areas such as medical imaging, precision medicine, and biosensors designed for the Internet of Things (IoT). Emphasis is placed on the latest progress in wearable biosensing technologies, where AI plays a pivotal role in monitoring electro-physiological and electro-chemical signals and aiding in disease diagnosis. These advancements underscore the growing trend towards personalized medicine, offering precise and costefficient point-of-care treatment. Additionally, the article delves into the advancements in computing technologies, including accelerated AI, edge computing, and federated learning specifically tailored for medical data. The challenges associated with data-driven AI approaches, potential issues arising from biosensors and IoT-based healthcare, and distribution shifts among different data modalities are thoroughly explored. The discussion concludes with insights into future prospects in the field.