In recent years, advancements in big data availability and computer computing power have driven significant progress in deep learning, particularly in image recognition, speech recognition, and natural language processing. These new developments have also provided new directions for the development of medicine, such as intelligent medical consultation, personalized diagnosis and treatment plan planning, medical image-assisted interpretation, etc. However, deep learning faces challenges such as weak reusability and insufficient interpretability in specific applications. This article reviews the development history of deep learning and some of its latest developments in the field of medicine, and examines some of its problems in medical applications.

In recent years, advancements in big data availability and computer computing power have driven significant progress in deep learning, particularly in image recognition, speech recognition, and natural language processing. These new developments have also provided new directions for the development of medicine, such as intelligent medical consultation, personalized diagnosis and treatment plan planning, medical image-assisted interpretation, etc. However, deep learning faces challenges such as weak reusability and insufficient interpretability in specific applications. This article reviews the development history of deep learning and some of its latest developments in the field of medicine, and examines some of its problems in medical applications.