Welcome to Special Session at ICONIP 2025!

Recent advances in transformer architectures have inspired new ways to apply artificial intelligence (AI) to medicine, healthcare, pharmacology, and biomedical sciences. Core to this advancement and application is the design of novel techniques to address the limitation of traditional machine learning and deep learning architectures in computer vision and language processing. On the other hand, demonstrating the outstanding application of this progress has a tendency to promote the widespread use of vision transformers in the medical field in the context of model interpretability, model complexity, and the need for extensively annotated datasets. They promise to advance capturing complex chemical features in drug design, and also evolvement of drug design landscapes. The models’ capability to capture long-range dependencies of complex relationships in medical imaging, protein sequences, and gene expression data, points to the prospect of further research in this area. Novel techniques are now required to tackle real-world challenges in medical image and language processing. In this ICONIP2025 Special Session, we encourage researchers to contribute their work, focusing on the design of algorithms, architectural innovative techniques, and application of the transformer and associated deep learning architectures. We also welcome studies demonstrating the combination of these models with other computational methods.