Abstract: With the wide application of machine learning technology, deep learning has achieved a lot as an emerging branch of machine learning. Due to its advantages in automatic feature learning and function simulation construction, deep learning has made great contributions in the fields of image recognition and natural language processing. To understand the existing research progress of deep learning in the field of biomedicine, this paper studies the related achievements of deep learning in this field through literature investigation. It is found that the application of deep learning in this field mainly focuses on bioinformatics, medical image recognition, disease prediction, clinical assisted decision-making and drug development, etc. There have been studies that transform information such as texts, images, and signals into multi-dimensional vectors. These studies use deep learning to develop a variety of models that are capable of learning data features, mining information and simulating status, so as to realize identification, evaluation, prediction and some other functions. With its advantages of complex simulation algorithms, deep learning has achieved better results than traditional algorithms in the field of biomedicine, and its application in areas such as automatic disease coding, integrated analysis of multiple data sources, and public health are worthy of further exploration.

Key words: Deep Learning, Machine Learning, Biomedicine, Medicine