The intersection of neural networks and healthcare has reached a pivotal moment, marking both the achievements and persistent challenges in biomedical AI. While traditional deep learning methods have significantly advanced the field, limitations such as interpretability, computational demands, and the complexities of handling multi-modal medical data have hindered widespread clinical adoption. This workshop seeks to address these challenges by exploring innovative neural architectures, real-time processing techniques, multimodal integration approaches, and methods for enhancing trust and interpretability in healthcare AI systems.
Explore novel neural architectures for biomedical applications
Address challenges in real-time processing of medical data
Develop strategies for multimodal data integration
Enhance trust and interpretability in healthcare AI systems
Bridge the gap between research innovations and clinical practice