Track 1
- AI for Medical Image Analysis
- Multimodal Learning for Biomedical Data
- Explainable and Trustworthy AI for Clinical Applications
- Self-Supervised and Transfer Learning in Biomedical Engineering
- Graph Neural Networks for Biomedical Data
- Generative AI and Synthetic Biomedical Data
- AI for Electronic Health Records and Clinical Data
- AI for Disease Prediction and Early Diagnosis
- Automated Machine Learning and Deep Learning for Healthcare Applications
Track 2
- Neural Networks for Biomedical Signal and Image Processing
- Spiking Neural Networks and Neuromorphic Biomedical Computing
- Computational Neuroscience and Neural Modeling
- Brain–Computer Interfaces and Neural Engineering
- Hybrid Neuro-Fuzzy Systems for Biomedical Applications
- Transformer and Attention Models for Biomedical Data
- Self-Supervised Learning for Neural and Physiological Signals
- Intelligent Prosthetics and Assistive Technologies
- Human–Machine Interfaces for Healthcare
Track 3
- AI-Enabled Biomedical and Surgical Robotics
- Rehabilitation and Assistive Robotics
- Human–Robot Interaction in Healthcare
- Autonomous and Multi-Agent Healthcare Systems
- Human–AI Interaction and Clinical Decision Support
- Wearable and Implantable Intelligent Systems
- Smart Sensors and Internet of Medical Things
- Digital Health and Mobile Health Technologies
- Healthcare Automation and Personalized Care
Track 4
- Explainable and Interpretable AI in Healthcare
- Privacy-Preserving Biomedical AI
- Secure AI and Cybersecurity in Medical Systems
- Robust and Reliable AI for Clinical Applications
- Human-Centered AI in Healthcare
- Digital Twins and Computational Models of Human Health
- AI for Clinical Workflow Optimization
- Translational Biomedical AI and Real-World Deployment