False results and time delay are longstanding challenges in biosensing. While classification
models and deep learning may provide new opportunities for improving biosensor …

Here, we provide a new methodology for reducing false results and time delay of
biosensors, which are barriers to industrial, healthcare, military, and consumer applications …

Since their inception, biosensors have frequently employed simple regression models to
calculate analyte composition based on the biosensor's signal magnitude. Traditionally …

The rapid development of biosensing technologies together with the advent of deep learning
has marked an era in healthcare and biomedical research where widespread devices like …

Sensing systems necessitate automation to reduce human effort, increase reproducibility,
and enable remote sensing. In this perspective, we highlight different types of sensing …

Over the past few decades, there has been tremendous interest in developing biosensing
systems that combine high sensitivity and specificity with rapid sample-to-answer times …

Recent advances in noninvasive portable and wearable biosensors have attracted
significant attention due to their capability to offer continual physiological information for …

Recently, Open AI's ChatGPT delivered ground-breaking news that is set to revolutionize our
way of life. This will transform the way we approach information search and connection …

In order to ensure the optimal functionality of biosensor devices across a diverse range of
applications, it is crucial to accurately predict their detection times. This study delves into an …

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the
potential to revolutionize diagnostic methodologies by offering rapid, accurate, and …