Applications of Deep Neural Networks in Biomedical Informatics

Dear Colleagues,

Deep neural networks (DNNs) have emerged as transformative tools in biomedical informatics, enabling breakthroughs in healthcare through their capacity to analyze complex, high-dimensional data such as medical images, genomics, electronic health records (EHRs), and drug compounds.

Advances in architectures like convolutional neural networks (CNNs), graph neural networks (GNNs), and transformer models have driven innovations in disease diagnosis, drug discovery, and personalized medicine, while techniques such as federated learning and multimodal data fusion address challenges of data privacy and holistic patient modeling. However, persistent hurdles—including data scarcity, model interpretability, ethical concerns, and integration into clinical workflows—underscore the need for interdisciplinary collaboration to translate algorithmic research into real-world impacts.

This Special Issue seeks to bridge these gaps by highlighting cutting-edge methodologies and applications, spanning technical advances (e.g., self-supervised learning and explainable AI), biomedical use cases (e.g., automated medical imaging analysis, clinical NLP, precision oncology), and translational considerations (e.g., regulatory compliance and bias mitigation). By uniting computational innovation with clinical and biological expertise, this issue aims to accelerate the development of robust, equitable, and deployable AI solutions that redefine healthcare delivery and patient outcomes.

Dr. Wenming Cao
Dr. Man-Fai Leung
Dr. Dan Dai
Guest Editors

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