Artificial Intelligence and Biosensors in Healthcare and its Clinical
Relevance: A Review
Abstract
Data generated from sources such as wearable sensors, medical imaging,
personal health records, pathology records, and public health
organizations have resulted in a massive information increase in the
medical sciences over the last decade. Advances in computational
hardware, such as cloud computing, Graphical Processing Units (GPUs),
and Tensor Processing Units (TPUs), provide the means to utilize these
data. Consequently, many Artificial Intelligence (AI)-based methods
have been developed to infer from large healthcare data. Here, we
present an overview of recent progress in artificial intelligence and
biosensors in medical and life sciences. We discuss the role of machine
learning in medical imaging, precision medicine, and biosensors for the
Internet of Things (IoT). We review the most recent advancements in
wearable biosensing technologies that use AI to assist in monitoring
bodily electro-physiological and electro-chemical signals and disease
diagnosis, demonstrating the trend towards personalized medicine with
highly effective, inexpensive, and precise point-of-care treatment.
Furthermore, an overview of the advances in computing technologies,
such as accelerated artificial intelligence, edge computing, and
federated learning for medical data, are also documented. Finally, we
investigate challenges in data-driven AI approaches, the potential
issues that biosensors and IoT-based healthcare generate, and the
distribution shifts that occur among different data modalities,
concluding with an overview of future prospects