Advanced Methods and Applications of Deep Learning in Medicine

Dear Colleagues,

Modern medicine is undergoing a profound data‑driven transformation. The growing complexity of clinical diagnosis and treatment, health monitoring, and biomedical research has produced vast, heterogeneous datasets, such as medical images, electronic health records, wearable device signals, laboratory test results, and multi‑omics data. While these data contain critical signals for early disease detection, risk stratification, and personalized therapy, they also pose significant challenges, including high dimensionality, elevated noise levels, sparse annotations, and fragmentation across institutions, which limit the effectiveness of traditional analytical methods.

Deep learning has rapidly emerged as a transformative technology by automatically learning hierarchical representations from raw or low‑level features, thereby reducing reliance on manual feature engineering and enhancing modeling capacity. In medicine, deep learning has shown substantial promise across domains, convolutional neural networks reach or exceed expert performance in image classification, segmentation, and detection; recurrent and attention‑based models capture temporal disease dynamics in electronic health records; and graph neural networks and multimodal fusion techniques integrate imaging, clinical, and omics data to support richer patient profiling and individualized treatment. Advances in self‑supervised pretraining, transfer learning, and federated learning are further improving model robustness, generalization, and trustworthiness in data‑scarce or distributed settings.

This Special Issue focuses on the mathematical foundations, algorithmic innovations, and medical applications of deep learning and computational intelligence. It aims to establish a platform for sharing cutting-edge research and innovative concepts, thereby fostering interdisciplinary exchange and collaboration.

Dr. Nan Sheng
Dr. Qiujie Lv
Prof. Dr. Yan Wang
Guest Editors

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