  Machine learning (ML) empowers biomedical systems with the capability to
optimize their performance through modeling of the available data extremely
well, without using strong assumptions about the modeled system. Especially in
nano-scale biosystems, where the generated data sets are too vast and complex
to mentally parse without computational assist, ML is instrumental in analyzing
and extracting new insights, accelerating material and structure discoveries,
and designing experience as well as supporting nano-scale communications and
networks. However, despite these efforts, the use of ML in nano-scale
biomedical engineering remains still under-explored in certain areas and
research challenges are still open in fields such as structure and material
design and simulations, communications and signal processing, and bio-medicine
applications. In this article, we review the existing research regarding the
use of ML in nano-scale biomedical engineering. In more detail, we first
identify and discuss the main challenges that can be formulated as ML problems.
These challenges are classified into the three aforementioned main categories.
Next, we discuss the state of the art ML methodologies that are used to
countermeasure the aforementioned challenges. For each of the presented
methodologies, special emphasis is given to its principles, applications, and
limitations. Finally, we conclude the article with insightful discussions, that
reveal research gaps and highlight possible future research directions.
