Application of Deep Learning in Clinical Diagnosis

Abstract

Deep learning has currently demonstrated tremendous potential and application value in the field of medical diagnosis. This article provides an overview of the latest advancements in deep learning technologies, particularly in areas such as multimodal fusion, interpretability, and edge computing. Subsequently, this article analyzes the challenges that deep learning faces in medical diagnosis, including limitations in aspects such as data ecology, technical implementation, and clinical application. At the same time, in response to these challenges, this paper also proposes corresponding solutions, such as developing hybrid architectures, building a federated learning ecosystem, and establishing human-machine collaborative adaptive workflows. Finally, the article also looks forward to the future development trends of deep learning in medical diagnosis and emphasizes the importance of interdisciplinary research and technological innovation.